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Who Moved My Data? Part 2: Insourcing Condition Monitoring

In my previous blog on this topic, “Who Moved My Data? Outsourcing Condition Monitoring,” I established the case for condition-based monitoring of critical assets to ensure a reduction in unplanned downtime. I also explored the advantages and disadvantages of outsourcing condition monitoring from critical assets. Here I discuss the do-it-yourself (DIY) approach to condition monitoring and explore its advantages and disadvantages.

Understanding the DIY approach

Now, let me be clear to avoid any confusion, when I refer to “do it yourself,” I don’t mean literally doing it yourself. Instead, this is something you own and customize to fit your applications. It may require a fair amount of input from your maintenance teams and plants. It’s not a one-day job, of course, but an ongoing initiative to help improve productivity and have continuous improvements throughout the plant.

Advantages and disadvantages of DIY condition monitoring (insourcing)

Implementing the solutions for continuous condition monitoring of critical assets by yourself has many advantages, along with some disadvantages. Let’s review them.

Advantages of insourcing (DIY) condition monitoring:

    1. Data ownership: One of the greatest benefits or advantages of implementing the DIY approach to condition monitoring is the control it gives you over data. You decide where the data lives, how it is used, and who has access to it. As I emphasized in my previous blog and numerous presentations on this topic, “Data is king” – a highly valuable commodity.
    2. Flexibility and customization: Of course, the DIY solution is not a one-size-fits-all approach! Instead, it allows you to customize the solution to fit your exact needs – the parameters to monitor, the specific areas of the plant to focus on the critical systems and the method of monitoring. You choose how to implement the solutions to fit your plant’s budget.
    3. Low long-term costs: As you own the installations, you own the data and you own the equipment; you don’t need to pay rent for the systems implemented through outsourcing.
    4. The specification advantage: As a plant or company, you can add condition monitoring features as specifications for your next generation of machines and equipment, including specific protocols or components. This allows you to collect the required data from the machine or equipment from the get-go.

Disadvantages of insourcing (DIY) condition monitoring:

    1. High upfront cost: Implementing condition monitoring with a data collection system may involve higher upfront costs. This is because there is a need to invest in data storage solutions, engage experts for condition monitoring implementation (typically from an integration house or through self-integration), and employ developers to create or customize dashboards to fit user needs.
    2. Limited scalability: collecting more data requires additional storage and enhanced analytics capabilities, especially when transitioning from condition-based maintenance to predictive analytics. Designing your own solution with limited budgets may hamper the scalability of the overall system.
    3. Infrastructure maintenance: This is another area that requires close attention. Whether the infrastructure is located on-premises, centralized, or in the cloud, the chosen location may require investments in manpower for ongoing maintenance.

Another point to emphasize here is that opting for a DIY solution does not preclude the use of cloud platforms for data management and data storage. The difference between insourcing and outsourcing lies in the implementation of condition monitoring and related analytics – whether it’s carried out and owned by you or by someone else.

Strategic decision-making: beyond cost considerations

The final point is not to make outsourcing decisions solely based on cost. Condition-based monitoring and the future of analytics offer numerous advantages, and nurturing an in-house culture could be a great source of competitive advantage for the organization. You can always start small and progressively expand.

As always, your feedback is welcome.

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Who Moved My Data? Outsourcing Condition Monitoring

This is the first in a three-part blog series on condition monitoring.

 

Critical assets are the lifeblood of the manufacturing plant. They are the devices, machines, and systems that when broken down or not performing to expected standards, can cause downtimes and production or quality losses resulting in rejects. If not maintained at the optimal levels of performance, these assets can damage the overall reputation of the brand. Some examples include evaporate fans, presses, motors, conveyor lines, mixers, grinders, and pumps.

Most manufacturing plants maintain critical assets on a periodic schedule, also known as preventative maintenance. However, in recent years, condition-based maintenance strategies, made possible with advancements in sensor and communications technologies, further improve the uptime, lower the overall cost of maintenance, and enhance the life of critical assets. Condition-based maintenance relies on continuous monitoring of key parameters of these assets.

Once the plant decides to adopt predictive maintenance (PdM) strategies for maintaining the assets, they face an important decision: to implement the condition monitoring strategy in-house or to outsource it to a third party – new term – continuous condition monitoring as a service (CCMAAS).

The bipartisan view expressed in this three-part blog series explores these options to help plant managers make the best, most appropriate decision for their plants. Just a hint: the decision for the most part is based on who controls the data regarding your plant’s critical assets.

In this part, we will delve a little deeper into the advantages and disadvantages of the CCMAAS option.

The advancements in cloud-based data management enable businesses to offer remote monitoring of the data related to the assets. In a nutshell, the service providers will audit the plant’s needs and deploy sensors and devices in the plant. Then, using IoT gateways, they transfer the critical parameters about the assets, such as vibration, temperature, humidity, and other related parameters to the cloud-based storage. The service provider’s proprietary algorithms and expertise would synthesize the data and send the plant’s maintenance personnel alerts about maintenance.

Advantages of outsourcing condition monitoring:

    1. Expertise and support: By outsourcing data management to a specialized provider, the plant has access to a team of experts who possess in-depth knowledge of condition monitoring and data analytics. These professionals can provide valuable insights, guidance, and technical support.
    2. Scalability and flexibility: Outsourced solutions offer greater scalability, allowing businesses to easily accommodate changing monitoring requirements and fluctuating data volumes.
    3. Cost reduction: Outsourcing eliminates the need for upfront investments in hardware and infrastructure, significantly reducing capital expenses. Instead, companies pay for services based on usage, making it a more predictable and manageable operational expense.

Disadvantages of outsourcing condition monitoring:

    1. Data security concerns: Entrusting critical data to a third-party provider raises concerns about data security and confidentiality. Plants must thoroughly assess the provider’s security protocols, data handling practices, and compliance with industry regulations to mitigate these risks.
    2. Dependency on service providers: Outsourcing data management means relying on external entities. If the service provider has technical difficulties, interruptions in service, or business-related issues, it may impact the organization’s operations and decision-making.
    3. Potential data access and control limitations: Plants may face limitations in accessing and controlling their data in real time. Reliance on a service-level agreement with the provider for data access, retrieval, or system upgrades can introduce delays or restrict autonomy.

Just like critical assets are the lifeblood of the manufacturing plants, in the near future data that is being generated every second in the plant will also be equally important. Outsourcing does allow manufacturing plants to adapt quickly to the new normal in the industry.  I would not completely discount outsourcing based on the control of data. The option does have its place. You will just have to wait for my concluding blog on this topic.

In the meantime, your feedback is always welcome.

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Why Invest in Smart Manufacturing Practices?

We’re all privy to talks about smart manufacturing, smart factory, machine learning, IIOT, ITOT convergence, and so on, and many manufacturers have already embarked on their smart manufacturing journeys. Let’s take a pause and really think about it… Is it really important or is it a fad? If it is important, then why?

In my role traveling across the U.S. meeting various manufacturers and machine builders, I often hear about their needs to collect data and have certain types of interfaces. But they don’t know what good that data is going to do. Well, let’s get down to the basics and understand this hunger for data and smart manufacturing.

Manufacturing goals

Since the dawn of industrialization, the industry has been focused on efficiency – always addressing issues of how to produce more, better and quicker. The goal of manufacturing always revolved around these four things:

    1. Reduce total manufacturing and supply chain costs
    2. Reduce scrap rate and improve quality
    3. Improve/increase asset utilization and machine availability
    4. Reduced unplanned downtime

Manufacturing megatrends

While striving for these goals, we have made improvements that have tremendously helped us as a society to improve our lifestyle. But we are now in a different world altogether. The megatrends that are affecting manufacturing today require manufacturers to be even more focused on these goals to stay competitive and add to their bottom lines.

The megatrends affecting the whole manufacturing industry include:

    • Globalization: The competition for a manufacturer is no longer local. There is always somebody somewhere making products that are cheaper, better or more available to meet demand.
    • Changing consumer behavior: I am old enough to say that, when growing up, there were only a handful of brands and only certain types of products that made it over doorsteps. These days, we have variety in almost every product we consume. And, our taste is constantly changing.
    • Lack of skilled labor: Almost every manufacturer that I talk to expresses that keeping and finding good skilled people has been very difficult. The baby boomers are retiring and creating huge skills gaps in the workplaces.
    • Aging equipment: According to one study, almost $65B worth of equipment in the U.S. is outdated, but still in production. Changing regulations require manufacturers to track and trace their products in many industries.

Technology has always been the catalyst for achieving new heights in efficiency. Given the megatrends affecting the manufacturing sector, the need for data is dire. Manufacturers must make decisions in real-time and having relevant and useful data is a key to success in this new economy.

Smart manufacturing practices

What we call “smart manufacturing practices” are practices that use technology to affect how we do things today and improve them multifold. They revolve around three key areas:

    1. Efficiency: If a line is down, the machine can point directly to where the problem is and tell you how to fix it. This reduces downtime. Even better is using data and patterns about the system to predict when the machine might fail.
    2. Flexibility: Using technology to retool or change over the line quickly for the next batch of production or responding to changing consumer tastes through adopting fast and agile manufacturing practices.
    3. Visibility: Operators, maintenance workers, and plant management all need a variety of information about the machine, the line, or even the processes. If we don’t have this data, we are falling behind.

In a nutshell, smart manufacturing practices that focus on one or more of these key areas, helps manufacturers boost productivity and address challenges presented by the megatrends. Hence, it is important to invest in these practices to stay competitive.

One more thing: There is no finish line when it comes to smart manufacturing. It should become a part of your continuous improvement program to evaluate and invest in technology that offers you more visibility, improves efficiency, and adds more flexibility to how you do things.

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Predictive Maintenance vs. Predictive Analytics, What’s the Difference?

With more and more customers getting onboard with IIoT applications in their plants, a new era of efficiency is lurking around the corner. Automation for maintenance is on the rise thanks to a shortage of qualified maintenance techs coinciding with a desire for more efficient maintenance, reduced downtime, and the inroads IT is making on the plant floor. Predictive Maintenance and Predictive Analytics are part of almost every conversation in manufacturing these days, and often the words are used interchangeably.

This blog is intended to make the clear distinction between these phrases and put into perspective the benefits that maintenance automation brings to the table for plant management and decision-makers, to ensure they can bring to their plants focused innovation and boost efficiencies throughout them.

Before we jump into the meat of the topic, let’s quickly review the earlier stages of the maintenance continuum.

Reactive and Preventative approaches

The Reactive and Preventative approaches are most commonly used in the maintenance continuum. With a Reactive approach, we basically run the machine or line until a failure occurs. This is the most efficient approach with the least downtime while the machine or line runs. Unfortunately, when the machine or line comes to a screeching stop, it presents us with the most costly of downtimes in terms of time wasted and the cost of machine repairs.

The Preventative approach calls for scheduled maintenance on the machine or line to avoid impending machine failures and reduce unplanned downtimes. Unfortunately, the Preventative maintenance strategy does not catch approximately 80% of machine failures. Of course, the Preventative approach is not a complete waste of time and money; regular tune-ups help the operations run smoother compared to the Reactive strategy.

Predictive Maintenance vs. Predictive Analytics

As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes. The idea behind Predictive Maintenance (PdM), aka condition-based maintenance, is that by frequently monitoring critical components of the machine, such as motors, pumps, or bearings, we can predict the impending failures of those components over time. Hence, we can prevent the failures by scheduling planned downtime to service machines or components in question. We take action based on predictive conditions or observations. The duration between the monitored condition and the action taken is much shorter here than in the Predictive Analytics approach.

Predictive Analytics, the next higher level on the maintenance continuum, refers to collecting the condition-based data over time, marrying it with expert knowledge of the system, and finally applying machine learning or artificial intelligence to predict the event or failure in the future. This can help avoid the failure altogether. Of course, it depends on the data sets we track, for how long, and how good our expert knowledge systems are.

So, the difference between Predictive Maintenance and Predictive Analytics, among other things, is the time between condition and action. In short, Predictive Maintenance is a stepping-stone to Predictive Analytics. Once in place, the system monitors and learns from the patterns to provide input on improving the system’s longevity and uptime. Predictive Maintenance or Preventative Maintenance does not add value in that respect.

While Preventative Maintenance and Predictive Maintenance promises shorter unplanned downtimes, Predictive Analytics promises avoidance of unplanned downtime and the reduction of planned downtime.

The first step to improving your plant floor OEE is with monitoring the conditions of the critical assets in the factory and collecting data regarding the failures.

Other related Automation Insights blogs:

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Controls Architectures Enable Condition Monitoring Throughout the Production Floor

In a previous blog post we covered some basics about condition monitoring and the capability of smart IO-Link end-devices to provide details about the health of the system. For example, a change in vibration level could mean a failure is near.

This post will detail three different architecture choices that enable condition monitoring to add efficiency to machines, processes, and systems: in-process, stand-alone, and hybrid models.

IO-Link is the technology that enables all three of these architectures. As a quick introduction, IO-Link is a data communications technology at the device level, instead of a traditional signal communication. Because it communicates using data instead of signals, it provides richer details from sensors and other end devices. (For more on IO-Link, search the blog.)

In-process condition monitoring architecture

In some systems, the PLC or machine controller is the central unit for processing data from all of the devices associated with the machine or system, synthesizing the data with the context, and then communicating information to higher-level systems, such as SCADA systems.

The data collected from devices is used primarily for controls purposes and secondarily to collect contextual information about the health of the system/machine and of the process. For example, on an assembly line, an IO-Link photo-eye sensor provides parts presence detection for process control, as well as vibration and inclination change detection information for condition monitoring.

With an in-process architecture, you can add dedicated condition monitoring sensors. For example, a vibration sensor or pressure sensor that does not have any bearings on the process can be connected and made part of the same architecture.

The advantage of an in-process architecture for condition monitoring is that both pieces of information (process information and condition monitoring information) can be collected at the same time and conveyed through a uniform messaging schema to higher-level SCADA systems to keep temporal data together. If properly stored, this information could be used later for machine improvements or machine learning purposes.

There are two key disadvantages with this type of architecture.

First, you can’t easily scale this system up. To add additional sensors for condition monitoring, you also need to alter and validate the machine controller program to incorporate changes in the controls architecture. This programming could become time consuming and costly due to the downtime related to the upgrades.

Second, machine controllers or PLCs are primarily designed for the purposes of machine control. Burdening these devices with data collection and dissemination could increase overall cost of the machine/system. If you are working with machine builders, you would need to validate their ability to offer systems that are capable of communicating with higher-level systems and Information Technology systems.

Stand-alone condition monitoring architecture

Stand-alone architectures, also known as add-on systems for condition monitoring, do not require a controller. In their simplest form, an IO-Link master, power supply, and appropriate condition monitoring sensors are all that you need. This approach is most prevalent at manufacturing plants that do not want to disturb the existing controls systems but want to add the ability to monitor key system parameters. To collect data, this architecture relies on Edge gateways, local storage, or remote (cloud) storage systems.

 

 

 

 

 

 

The biggest advantage of this system is that it is separate from the controls system and is scalable and modular, so it is not confined by the capabilities of the PLC or the machine controller.

This architecture uses industrial-grade gateways to interface directly with information technology systems. As needs differ from machine to machine and from company to company as to what rate to collect the data, where to store the data, and when to issue alerts, the biggest challenge is to find the right partner who can integrate IT/OT systems. They also need to maintain your IT data-handling policies.

This stand-alone approach allows you to create various dashboards and alerting mechanisms that offer flexibility and increased productivity. For example, based on certain configurable conditions, the system can send email or text messages to defined groups, such as maintenance or line supervisors. You can set up priorities and manage severities, using concise, modular dashboards to give you visibility of the entire plant. Scaling up the system by adding gateways and sensors, if it is designed properly, could be easy to do.

Since this architecture is independent of the machine controls, and typically not all machines in the plant come from the same machine builders, this architecture allows you to collect uniform condition monitoring data from various systems throughout the plant. This is the main reason that stand-alone architecture is more sought after than in-process architecture.

It is important to mention here that not all of the IO-Link gateways (masters) available in the market are capable of communicating directly with the higher-level IT system.

Hybrid architectures for condition monitoring

As the name suggests, this approach offers a combination of in-process and stand-alone approaches. It uses IO-Link gateways in the PLC or machine controller-based controls architecture to communicate directly with higher-level systems to collect data for condition monitoring. Again, as in stand-alone systems, not all IO-Link gateways are capable of communicating directly with higher-level systems for data collection.

The biggest advantage of this system is that it does not burden PLCs or machine controllers with data collection. It creates a parallel path for health monitoring while devices are being used for process control. This could help you avoid duplication of devices.

When the devices are used in the controls loop for machine control, scalability is limited. By specifying IO-Link gateways and devices that can support higher-level communication abilities, you can add out-of-process condition monitoring and achieve uniformity in data collection throughout the plant even though the machines are from various machine builders.

Overall, no matter what approach is the best fit for your situation, condition monitoring can provide many efficiencies in the plant.

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How Condition Monitoring has Evolved and Its Role in IIoT

In recent years, as IIoT and Industry 4.0 have become part of our everyday vocabulary, we’ve also started hearing more about condition monitoring, predictive maintenance (PdM) and predictive analytics. Sometimes, we use these terms interchangeably as well. Strictly speaking, condition monitoring is a root that enables both predictive maintenance and predictive analytics. In today’s blog we will brush up a little on condition monitoring and explore its lineage.

Equipment failures have been around since the beginning of time. Over the years, through observation (collecting data) and brute-force methods, we learned that from time-to-time every piece of equipment needs some TLC. Out of this understanding, maintenance departments came to existence, and there we started having experts that could tell based on touch, smell and noise what is failing or what has gone wrong.

Figure 1: Automation Pyramid

Then we started automating the maintenance function either as a preventative measure (scheduled maintenance) or through some automated pieces of equipment that would collect data and provide alerts about a failure. We proudly call these SCADA systems – Supervisory Control and Data Acquisition. Of course, these systems did not necessarily prevent failures, but help curtail them.  If we look at the automation pyramid, the smart system at the bottom is a PLC and all the sensors are what we call “dumb sensors”. So, that means, whatever information the SCADA system gets would be filtered by the PLC. PLCs were/have been/ and are always focused on the process at hand; they are not data acquisition equipment. So, the data we receive in the SCADA system is only as good as the PLC can provide. That means the information is primarily about processes. So, the only alerts maintenance receives is when the equipment fails, and the process comes to a halt.

With the maintenance experts who could sense impending failures becoming mythological heroes, and  SCADA systems that cannot really tell us the story about the health of the machines, once again, we are looking at condition monitoring with a fresh set of eyes.

Sensors are at the grass root level in the automation pyramid, and until the arrival of IO-Link technology, these sensors were solely focused on their purpose of existence; object detection, or measurement of some kind. The only information one could gather from these sensors was ON/OFF or a signal of 4-20mA, 0-10V, and so on. Now, things are different, these sensors are now becoming pretty intelligent and they, like nosy neighbors, can collect more information about their own health and the environment. These intelligent sensors can utilize IO-Link as a communication to transfer all this information via a gateway module (generally known as IO-Link master) to whomever wants to listen.

Figure 2: IO-Link enabled Balluff photo-eye

The new generation of SCADA systems can now collect information not only from PLCs about the process health, but also from individual devices. For example, a photo-eye can measure the intensity of the reflected light and provide an alert if the intensity drops beyond a certain level, indicating a symptom of pending failure. Or a power supply inside the cabinet providing an alert to the supervisory control about adverse conditions due to increase temperature or humidity in the cabinet. These types of alerts about the symptoms help maintenance prevent unplanned downtime on the plant floor and make factories run more efficiently with reduced scrap, reduced down-time and reduced headaches.

Figure 3: The Next Generation Condition Monitoring

There are many different condition monitoring architectures that can be employed, and we will cover that in my next blog.

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IO-Link Boosts Plant Productivity

In my previous blog, Using Data to Drive Plant Productivity, I categorized reasons for downtime in the plant and also discussed how data from devices and sensors could be useful in boosting productivity on the plant floor. In this blog, I will focus on where this data is and how to access it. I also touched on the topic of standardizing interfaces to help boost productivity – I will discuss this topic in my future blog.

Sensor technology has made significant progress in last two decades. The traditional transistor technology that my generation learned about is long gone. Almost every sensor now has at least one microchip and possibly even MEMs chips that allow the sensor to know an abundance of data about itself and the environment it which it resides. When we use these ultra-talented sensors only for simple signal communication, to understand presence/absence of objects, or to get measurements in traditional analog values (0-20mA, 0-10V, +5/-5V and so on), we are doing disservice to these sensors as well as keeping our machines from progressing and competing at higher levels. It is almost like choking the throat of the sensor and not letting it speak up.

To elaborate on my point, let’s take following two examples: First, a pressure sensor that is communicating 4-20mA signal to indicate pressure value. Now, that sensor can not only read pressure value but, more than likely, it can also register the ambient temperatures and vibrations. Although, the sensor is capable of understanding these other parameters, there is no way for it to communicate that information to the higher level controller. Due to this lack of ambient information, we may not be able to prevent some eminent failures. This is because of the choice of communication technology we selected – i.e. analog signal communication.

For the second example, let us take a simple photoeye sensor that only communicates presence/absence through discrete input and 0/1 signal. This photoeye also understands its environment and other more critical information that is directly related to its functionality, such as information about its photoelectric lens. The sensor is capable of measuring the intensity of re-emitted light, because based on that light intensity it is determining presence or absence of objects. If the lens becomes cloudy or the alignment of the reflector changes, it directly impacts the remitted light intensity and leads to sensor failure. Due to the choice of digital communication, there is no way for the sensor to inform the higher level control of this situation and the operator only learns of it when the failure happens.

If  a data communication technology, such as IO-Link, was used in these scenarios instead of signal communication, we could unleash these sensors to provide useful information about themselves as well as about their environment. If we collect this data or set alerts in the sensor for the upper/lower limits on this type of information, the maintenance teams would know in advance about the possible failures and prevent these failures to avoid eminent downtime.

Collecting this data at appropriate frequencies could help build a more relevant database and demonstrate patterns for the next generation of machine learning and predictive maintenance initiatives. This would be data driven continuous improvement to prevent failures and boost productivity.

The information collected from sensors and devices – so called smart devices – not only helps end users of automation to boost their plant’s productivity, but also helps machine builders to better understand their own machine usage and behaviors. Increased knowledge improves the designs for the next generation of machines.

If we utilized these smart sensors and devices at our change points in the machine, it would help fully or partially automate the product change-overs. With IO-Link as a technology, these sensors can be reconfigured and re-purposed for different applications without needing different sensors or manual tunings.

IO-Link technology has a built in feature called “automatic parameterization” that helps reduce plant down-time when sensors need replaced. This feature stores IO-Link devices’ configuration on the master port as well as all the configuration is also persistent in the sensor. Replacement is as simple as connecting the new sensor of the same type, and the IO-Link master downloads all the parameters and  replacement is complete.

Let’s recap:

  1. IO-Link unleashes a sensor’s potential to provide information about its condition as well as the ambient conditions, enabling condition monitoring, predictive maintenance and machine learning.
  2. IO-Link offers remote configuration of devices, enabling quick and automated change overs on the production line for different batches, reducing change over times and boosting plant productivity.
  3. IO-Link’s automatic parameterization feature simplifies device replacement, reducing unplanned down-time.

Hope this helps boost productivity of your plant!

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Using Data to Drive Plant Productivity

What is keeping us from boosting productivity in our plants to the next level? During a recent presentation on Industry 4.0 and IIoT, I was asked this question.

The single biggest thing, in my opinion, that is keeping us from boosting productivity to the next level is a lack of DATA. Specifically, data about the systems and the processes.

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Since the beginning of time, we have been hungry for efficiency. While early man invented more efficient methods to hunt and survive, today we are looking for ways to produce more efficiently in our plants with minimum or zero waste. After exhausting all the avenues for lean operations on plant procedures and our day-to-day activities, we are now looking at how we can recover from unanticipated downtime quickly. I am sure in future we will be seeking information on how can we prevent the downtime altogether.

There are plentiful of reasons for downtime. Just a few examples:

  1. Unavailability of labor – something we might be experiencing these days, when the COVID-19 pandemic has reduced some labor forces
  2. Unavailability of raw materials
  3. Unavailability of replacement components
  4. Unavailability of assets
  5. Failures in machines/components

In this list, the first two reasons, are beyond the scope of this blog’s intentions and frankly somewhat out of controls from the production standpoint.

The next two reasons, however, are process related and the last one is purely based on the choices we made. These three reasons, to a certain extent, can be reduced or eliminated.

If the downtime is process related, we can learn from them and improve our processes with so called continuous improvement initiatives. We can only do these continuous improvements based on observable factors (a.k.a. data) and we cannot improve our processes based on speculations. Well, I shouldn’t say “cannot”, but it will be more like a fluke or luck. It is apt to say “ what can’t be measured, can’t be improved!”

A good example for elaborating my point is change-over in the plant to produce a different product. Unless there is a good process in place for ensuring all the change-over points are properly addressed and all the change parts are correctly installed and replaced, the changeover time could and will likely lead to tremendous amounts of lost productivity. Secondly, if these processes are done manually and not automated, that is also a loss of productivity or, as I like to say, an area for continuous improvement to boost productivity based on observable facts. Sometimes, we take these manual change-overs as a fact of life and incorporate that time required as a part of “planned” downtime.  Of course, if you do change-overs once a year – it may be cost effective to keep the process manual even in today’s situation. But, if your plant has multiple short batch productions per day or per week, then automating the changeovers could significant boost productivity. The cost benefit analysis should help prove if it is continuous improvement or not.

Assets are an important part of the equation for smooth operations. An example would be molds in the stamping plant or cutting-deburring tools in metal working plants. If plants have no visibility or traceability of these important assets for location, shape or form, it could lead to considerable downtime. The calibration data of these tools or number of parts produced with the tool are also important pieces of data that needs to be maintained for efficient operations. Again, this is data about the system and the integration of these traceability initiatives in the existing infrastructure.

Failures in machines or components could cause severe downtime and are often considered as unavoidable. We tackle these failures in a two-step approach. First, we hunt for the problem when it is not obvious, and two, we find the replacement part in the store room to change it out quickly. And, as process improvement, we schedule preventative maintenance to inspect, lubricate and replace parts in our regular planned downtime.

The preventative maintenance is typically scheduled based on theoretical rate of failure. This is a good measure, especially for mechanical components, but, predictive or condition-based maintenance usually yields higher returns on productivity and helps keep plants running smooth. Again, predictive maintenance relies on data about the condition of the system or components. So, where is this data and how do we get to it?

Standardization of interfaces is another important component for boosting productivity. In my next blog, I will share how IO-Link as a technology can help address all of these challenges and boost productivity to the next level.

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Inductive Coupling: A Simple Solution for Replacing Slip Rings

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Figure 1: Inductive coupling for power and data exchange

In the industrial automation space, inductive sensors have grown very popular , most commonly used for detecting the proximity of metal objects such as food cans, or machine parts. Inductive coupling, also known as non-contact connectors, uses magnetic induction to transfer power and data over an air gap.

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Figure 2: Slip ring example

While inductive couplers have many uses, one of the most beneficial is for replacing a traditional slip-ring mechanism. Slip-rings, also known as rotary connectors, are typically used in areas of a machine where one part rotates, and another part of the machine remains stationary, such as a turn table where stations on the indexing table need power and I/O, but the table rotates a full 360°. This set up makes standard cable solutions ineffective.

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Figure 3: Inductive coupling replacing the slip-ring

A slip ring could be installed at the base of the table, but since they are electromechanical devices, they are subject to wear out. And unfortunately, the signs for wearing are not evident and often it is only a lack of power that alerts workers to an issue.

An inductive coupling solution eliminates all the hassle of the mechanical parts. With non-contact inductive coupling, the base of a coupler could be mounted at the base of the table and the remote end could be mounted on the rotating part of the table.

Additionally, slip rings are susceptible to noise and vibration, but because inductive couplers do not have contact between the base and the remote, they do not have this problem.

Inductive couplers are typically IP67-rated, meaning they are not affected by dirt or water, or  vibrations, and most importantly, they are contact free so no maintenance is necessary.

Learn more about Balluff inductive couplers www.balluff.us.

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IO-Link reduces waste due to sensor failures

In the last two blogs we discussed about Lean operations and reducing waste as well as Selecting right sensors for the job and the environment that the sensor will be placed. Anytime a sensor fails and needs a replacement, it is a major cause of downtime or waste (in Lean philosophy). One of the key benefits of IO-Link technology is drastically reducing this unplanned downtime and replacing sensors with ease, especially when it comes to measurement sensors or complex smart sensors such as flow sensors, continuous position monitoring sensors, pressure sensors, laser sensors and so on.

When we think about analog measurement sensor replacement, there are multiple steps involved. First, finding the right sensor. Second, calibrating the sensor for the application and configuring its setpoints. And third, hope that the sensor is functioning correctly.

Most often, the calibration and setpoint configuration is a manual process and if the 5S processes are implemented properly, there is a good chance that the procedures are written down and accessible somewhere. The process itself may take some time to be carried out, which would hold up the production line causing undesired downtime. Often these mission critical sensors are in areas of the machine that are difficult to access, making replacing then, let alone configuring, a challenge.

IO-Link offers an inherent feature to solve this problem and eliminates the uncertainty that the sensor is functioning correctly. The very first benefit that comes with sensors enabled with IO-Link is that measurement or readings are in engineering units straight from the sensor including bar, psi, microns, mm, liters/min, and gallons/min. This eliminated the need for measurements to be scaled and adjusted in the programming to engineering units.

Secondly, IO-Link masters offer the ability to automatically reconfigure the sensors. Many manufacturers call this out as automatic device replacement (ADR) or parameter server functionality of the master. In a nutshell, when enabled on a specific port of the multi-port IO-Link master, the master port reads current configuration from the sensor and locks them in. From that time forward, any changes made directly on the sensor are automatically overwritten by these locked parameters. The locked parameters can be accessed and changed only through authorized users. When the time comes to replace the sensor, there is only one step that needs to happen: Find the replacement sensor of the same model and plug it in. That’s it!

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When the new sensor is plugged-in, the IO-Link master automatically detects that the replacement sensor does not have the correct parameters and automatically updates them on the sensor. Since the readings are directly in the units desired, there is no magic of scaling to fiddle with.

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It is also important to note, that in addition to the ADR feature, there may be parameters or settings on the sensors that alert you to possible near-future failure of the sensor. This lets you avoid unplanned downtime due to sensor failure. A good example would be a pressure sensor that sends an alert (event) message indicating that the ambient temperature is too high or a photo-eye alerting the re-emitted light value is down close to threshold – implying that either the lens is cloudy, or alignment is off.

To learn more about IO-Link check out our other blogs.