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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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Using MQTT Protocol for Smarter Automation

In my previous blog post, “Edge Gateways to Support Real-Time Condition Monitoring Data,” I talked about the importance of using an edge gateway to gather the IoT data from sensors in parallel with a PLC. This was because of the large data load and the need to avoid interfering with the existing machine communications. In this post, I want to delve deeper into the topic and explain the process of implementing an edge gateway.

Using the existing Ethernet infrastructure

One way to collect IoT data with an edge gateway is by using the existing Ethernet infrastructure. With most devices already communicating on an industrial Ethernet protocol, an edge gateway can gather the data on the same physical Ethernet port but at a separate software-defined number associated to a network protocol communication.

Message Queue Telemetry Transport (MQTT)

One of the most commonly used IoT protocols is Message Queue Telemetry Transport (MQTT). It is an ISO standard and has a dedicated software Ethernet port of 1883 and 8883 for secure encrypted communications. One reason for its popularity is that it is designed to be lightweight and efficient. Lightweight means that the protocol requires a minimum coding and it uses low-bandwidth connections.

Brokers and clients

The MQTT protocol defines two entities: a broker and client. The edge gateway typically serves as a message broker that receives client messages and routes them to the appropriate destination clients. A client is any device that runs an MQTT library and connects to an MQTT broker.

MQTT works on a publisher and subscriber model. Smart IoT devices are set up to be publishers, where they publish different condition data as topics to an edge gateway. Other clients, such as PC and data centers, can be set up as subscribers. The edge gateway, serving as a broker receives all the published data and forwards it only to the subscribers interested in that topic.

One client can publish many different topics as well as be a subscriber to other topics. There can also be many clients subscribing to the same topic, making the architecture flexible and scalable.

The edge gateway serving as the broker makes it possible for devices to communicate with each other if the device supports the MQTT protocol. MQTT can connect a wide range of devices, from sensors to actuators on machines to mobile devices and cloud servers. While MQTT isn’t the only way to gather data, it offers a simple and reliable way for customers to start gathering that data with their existing Ethernet infrastructures.

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The Benefits of Mobile Handheld and Stationary Code Readers

Ensuring reliable traceability of products and assembly is critical in industries such as automotive, pharmaceuticals, and electronics. Code readers are essential in achieving this, with stationary and mobile handheld readers being the two most popular options. In what situations is it more appropriate to use one type over the other?

Stationary optical ID sensors

Stationary optical ID sensors offer simple and reliable code reading, making them an excellent option for ensuring traceability. They can read various codes, including barcodes, 2D codes, and DMC codes, and are permanently installed in the plant. Additionally, with their standardized automation and IT interfaces, the information readout can be passed on to the PLC or IT systems. Some variants also come with an IO-Link interface for extremely simple integration. The modern solution offers additional condition monitoring information, such as vibration, temperature, code quality, and operating time, making them a unique multi-talent within optical identification.

Portable code readers

Portable code readers provide maximum freedom of movement and can quickly and reliably read common 1D, 2D, and stacked barcodes on documents and directly on items. Various applications use them for controlling supply processes, production control, component tracking, quality control, and inventory. The wireless variants of handheld code readers with Bluetooth technology allow users to move around freely within a range of up to 100 meters around the base station. They also have a reliable read confirmation system via acoustic signal, LEDs, and a light spot projected onto the read code. Furthermore, the ergonomic design and highly visible laser marking frames ensure fatigue-free work.

Both stationary and mobile handheld barcode readers play an essential role in ensuring reliable traceability of products and assembly in various industries. Choosing the right type of barcode reader for your application is crucial to ensure optimal performance and efficiency. While stationary code readers are ideal for constant scanning in production lines, mobile handheld readers offer flexibility and reliability for various applications. Regardless of your choice, both devices offer simple operation and standardized automation and IT interfaces, making them essential tools for businesses that rely on efficient code reading.

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Automated Welding With IO-Link

IO-Link technologies have been a game-changer for the welding industry. With the advent of automation, the demand for increasingly sophisticated and intelligent technologies has increased. IO-Link technologies have risen to meet this demand. Here I explain the concepts and benefits of I-O Link technologies and how they integrate into automated welding applications.

What are IO-Link technologies?

IO-Link technologies refer to an advanced communication protocol used in industrial automation. The technology allows data transfer, i.e., the status of sensors, actuators, and other devices through a one-point connection between the control system and individual devices. Also, it enables devices to communicate among themselves quickly and efficiently. IO-Link technologies provide real-time communication, enabling continuous monitoring of devices to ensure optimal performance.

Benefits of IO-Link technologies

    • Enhanced data communication: IO-Link technologies can transfer data between the control system and sensors or devices. This communication creates an open and transparent network of information, reflecting the real-time status of equipment and allowing for increased reliability and reduced downtime.
    • Cost-efficiency: IO-Link technologies do not require complicated wiring and can significantly reduce material costs compared to traditional hardwired solutions. Additionally, maintenance is easier and more efficient with communication between devices, and there is less need for multiple maintenance employees to manage equipment.
    • Flexibility: With IO-Link technologies, the control system can control and monitor devices even when not attached to specific operator workstations. It enables one control system to manage thousands of devices without needing to rewrite programming to accommodate different machine types.
    • Real-time monitoring: IO-Link technologies provide real-time monitoring of devices, allowing control systems to monitor failures before they occur, making it easier for maintenance teams to manage the shop floor.

How are IO-Link technologies used in automated welding applications?

Automated welding applications have increased efficiencies and continuity in processes, and IO-Link technologies have accelerated these processes further. Automated welding applications have different stages, and each step requires real-time monitoring to ensure the process is efficient and effective. IO-Link technologies have been integrated into various parts of the automated welding process, some of which include:

    1. Positioning and alignment: The welding process starts with positioning and aligning materials such as beams, plates, and pipes. IO-Link sensors can detect the height and gap position of the material before the welding process begins. The sensor sends positional data to the control system as a feedback loop, which then adjusts the positioning system using actuators to ensure optimal weld quality.
    2. Welding arc monitoring: The welding arc monitoring system is another critical application for IO-Link technologies. Monitoring the arc ensures optimal weld quality and runs with reduced interruptions. IO-Link temperature sensors attached to the welding tip help control and adjust the temperature required to melt and flow the metal, ensuring that the welding arc works optimally.
    3. Power supply calibration: IO-Link technologies are essential in calibrating the power output of welding supplies, ensuring consistent quality in the welding process. Detectors attached to the power supply record the energy usage, power output and voltage levels, allowing the control system to adjust as necessary.
    4. Real-time monitoring and alerting: Real-time monitoring and alerting capabilities provided by IO-Link technologies help to reduce downtime where machine health is at risk. The sensors monitor the welding process, determining if there are any deviations from the set parameters. They then communicate the process condition to the control system, dispatching alerts to maintenance teams if an issue arises.

Using IO-Link technologies in automated welding applications has revolutionized the welding industry, providing real-time communication, enhanced data transfer, flexibility, and real-time monitoring capabilities required for reliable processes. IO-Link technologies have been integrated at various stages of automated welding, including positioning and alignment, welding arc monitoring, power supply calibration, and real-time monitoring and alerting. There is no doubt that the future of automated welding is bright. With IO-Link technologies, the possibilities are endless, forging ahead to provide more intelligent, efficient, and reliable welding applications.

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Demystifying Machine Learning

Machine learning can help organizations improve manufacturing operations and increase efficiency, productivity, and safety by analyzing data from connected machines and sensors, machine. For example, its algorithms can predict when equipment will likely fail, so manufacturers can schedule maintenance before problems occur, thereby reducing downtime and repair costs.

How machine learning works

Machine learning teaches computers to learn from data – to do things without being specifically told how to do them. It is a type of artificial intelligence that enables computers to automatically learn or improve their performances by learning from their experiences.

machine learning stepsImagine you have a bunch of toy cars and want to teach a computer to sort them into two groups: red and blue cars. You could show the computer many pictures of red and blue cars and say, “this is a red car” or “this is a blue car” for each one.

After seeing enough examples, the computer can start to guess which group a car belongs in, even if it’s a car that it hasn’t seen before. The machine is “learning” from the examples you show to make better and better guesses over time. That’s machine learning!

Steps to translate it to industrial use case

As in the toy car example, we must have pictures of each specimen and describe them to the computer. The image, in this case, is made up of data points and the description is a label. The sensors collecting data can be fed to the machine learning algorithm in different stages of the machine operation – like when it is running optimally, needs inspection, or needs maintenance, etc.

Data taken from vibration, temperature or pressure measures, etc., can be read from different sensors, depending on the type of machine or process to monitor.

In essence, the algorithm finds a pattern for each stage of the machine’s operation. It can notify the operator about what must be done given enough data points when it starts to veer toward a different stage.

What infrastructure is needed? Can my PLC do it?

The infrastructure needed can vary depending on the algorithm’s complexity and the data volume. Small and simple tasks like anomaly detection can be used on edge devices but not on traditional automation controllers like PLCs. Complex algorithms and significant volumes of data require more extensive infrastructure to do it in a reasonable time. The factor is the processing power, and as close to real-time we can detect the machine’s state, the better the usability.

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Using Guided Changeover to Reduce Maintenance Costs, Downtime

A guided changeover system can drastically reduce the errors involved with machine operation, especially when added to machines using fully automated changeovers. Processing multiple parts and recipes during a production routine requires a range of machines, and tolerances are important to quantify. Only relying on the human element is detrimental to profits, machine maintenance, and production volumes. Implementing operator assistance to guide visual guidance will reveal inefficiencies and allow for vast improvements.

Removing human error

Unverified manual adjustments may cause machine fatigue or failure. In a traditional manual changeover system, the frequency of machine maintenance is greater if proper tolerances are not observed at each changeover. Using IO-Link can remove the variable of human error with step-by-step instructions paired with precise sensors in closed-loop feedback. The machine can start up and run only when all parts are in the correct position.

Preventative maintenance and condition monitoring

Preventative maintenance is achievable with the assistance of sensors, technology, and systems. Using condition monitoring for motors, pumps and critical components can help prevent the need for maintenance and notably improve the effectiveness of maintenance with custom alerts and notifications with a highly useful database and graphing function.

A repeatable maintenance routine based on condition monitoring data and using a system to guide machine changeover will prolong machine life and potentially eliminate downtime altogether.

For more, read this real-world application story, including an automated format change to eliminate human error, reduce waste and decrease downtime.

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Machine Failures and Condition Monitoring – Selecting Sensors

In previous blogs, we discussed the different types of machine failures and their implications for different maintenance approaches, the cost-benefit tradeoffs of these maintenance approaches, and the progression of machine failures and indicators that emerge at various failure phases. We now will connect the different failure indicators to the sensors which can detect them.

The Potential – Functional Failure (P-F) curve gives a rough picture of when various indicators may emerge during the progression of a failure:

Each indicator can be detected by one or more types of sensors. Selection of the “best” sensor will depend on the machine/asset being monitored, other attributes being sensed, budget/cost-benefit tradeoff, and the maintenance approach. In some cases, a single-purpose, dedicated condition-monitoring sensor may be the right choice. In other cases, a multi-function sensor (“Smart Automation and Monitoring System sensor”) which can handle both condition monitoring and standard sensing tasks may be an elegant and cost-effective solution.

The table below gives some guidance to possible single- and multi-function sensors which can address the various indicators:

* Condition monitoring sensors are specialized sensors that can often detect multiple indicators including vibration, temperature, humidity, and ambient pressure.

# Smart Automation and Monitoring System sensors add condition monitoring sensing, such as vibration and temperature, to their standard sensing functions, such as photoelectric, inductive, or capacitive sensing

There is a wide range of sensors that can provide the information needed for condition monitoring indication. The table above can provide some guidance, selecting the best fit requires an evaluation of the application, the costs/benefits, and fit with the maintenance strategy.

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IO-Link Changeover: ID Without RFID – Hub ID

When looking at flexible manufacturing, what first comes to mind are the challenges of handling product changeovers. It is more and more common for manufacturers to produce multiple products on the same production line, as well as to perform multiple operations in the same space.

Accomplishing this and making these machines more flexible requires changing machine parts to allow for different stages in the production cycle. These interchangeable parts are all throughout a plant: die changes, tooling changes, fixture changes, end-of-arm tooling, and more.

When swapping out these interchangeable parts it is crucial you can identify what tooling is in place and ensure that it is correct.

ID without RFID

When it comes to identifying assets in manufacturing today, typically the first option companies consider is Radio-Frequency Identification (RFID). Understandably so, as this is a great solution, especially when tooling does not need an electrical connection. It also allows additional information beyond just identification to be read and written on the tag on the asset.

It is more and more common in changeover applications for tooling, fixtures, dies, or end-of-arm tooling to require some sort of electrical connection for power, communication, I/O, etc. If this is the case, using RFID may be redundant, depending on the overall application. Let’s consider identifying these changeable parts without incurring additional costs such as RFID or barcode readers.

Hub ID with IO-Link

In changeover applications that use IO-Link, the most common devices used on the physical tooling are IO-Link hubs. IO-Link system architectures are very customizable, allowing great flexibility to different varieties of tooling when changeover is needed. Using a single IO-Link port on an IO-Link master block, a standard prox cable, and hub(s), there is the capability of up to: 

    • 30 Digital Inputs/Outputs or
    • 14 Digital Inputs/Outputs and Valve Manifold Control or
    • 8 Digital Inputs/Outputs and 4 Analog Voltage/Current Signals or
    • 8 Analog Input Signals (Voltage/Current, Pt Sensor, and Thermocouple)

When using a setup like this, an IO-Link 1.1 hub (or any IO-Link 1.1 device) can store unique identification data. This is done via the Serial Number Parameter and/or Application Specific Tag Parameter. They act as a 16- or 32-byte memory location for customizable alphanumeric information. This allows for tooling to have any name stored within that memory location. For example, Fixture 44, Die 12, Tool 78, EOAT 123, etc. Once there is a connection, the controller can request the identification data from the tool to ensure it is using the correct tool for the upcoming process.

By using IO-Link, there are a plethora of options for changeover tooling design, regardless of various I/O requirements. Also, you can identify your tooling without adding RFID or any other redundant hardware. Even so, in the growing world of Industry 4.0 and the Industrial Internet of Things, is this enough information to be getting from your tooling?

In addition to the diagnostics and parameter setting benefits of IO-Link, there are now hub options with condition monitoring capabilities. These allow for even more information from your tooling and fixtures like:

    • Vibration detection
    • Internal temperature monitoring
    • Voltage and current monitoring
    • Operating hours counter

Flexible manufacturing is no doubt a challenge and there are many more things to consider for die, tooling and fixture changes, and end-of-arm tooling outside of just ID. Thankfully, there are many solutions within the IO-Link toolbox.

For your next changeover, I recommend checking out Non-Contact Inductive Couplers Provide Wiring Advantages, Added Flexibility and Cost Savings Over Industrial Multi-Pin Connectors for a great solution for non-contact connectivity that can work directly with Hub ID.

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Industrial Machinery Failure Types and Implications for Maintenance Approaches

Industrial machinery can fail in many different ways and for many different reasons. For critical and/or expensive equipment, it is a major challenge to find a way to detect potential failures before they happen and to take action to prevent or minimize the effects. Closely tied to this is the tradeoff between the cost of detection and the cost of failure. We discussed some of these tradeoffs in the blog “Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs.”

When assessing how equipment might fail, several industry studies* have identified six primary failure types which may be considered:

    • Type A: Lower probability of failure in early- and mid-life of the asset, with a dramatic increase in probability of failure in late-life. This is typical for mechanical devices, such as engines, fans, compressors, and machines.
    • Type B: Higher initial probability of failure when the asset is new, with a much lower/steady failure probability over the rest of the asset’s life. This is often the profile for electronic devices such as computers, PLCs, etc.
    • Type C: Lower initial probability of failure when the asset is new, with an increase to a steady failure probability in mid- and late-life. These are often devices with high stress work conditions, such as pressure relief valves.
    • Type D: Consistent probability of failure throughout the asset life, similar failure probability in early-, mid- and late-life. This can cover many types of industrial machines, often with stable, proven design and components.
    • Type E: Higher probability of failure in early- and late-life, a lower and constant probability of failure in mid-life (often called a “bathtub curve”). This can be devices that “settle in” after a wear-in period and then experience higher failures at the end of life, such as bearings.
    • Type F: Lower probability of failure when new, with a gradual increase over time and without the steep increase in failure probability at the end of life of Type A. This is often typical where age-based wear is steady and gradual in equipment such as turbine engines and structural components (pressure vessels, beams, etc.).

Age-related and non-age-related failures

These six failure types fall into two categories: age-related and non-age-related failures. The studies show that 15-30% of failures are age-related (Types A, E & F) and 70-85% of failures are non-age-related (Types B, C & D). The age-related failures have a clear correlation between the age of the asset and the likelihood of failure. In these cases, preventative maintenance at regular time-based intervals may be appropriate and cost-effective. The non-age-based failures are more “random,” due to improper design/installation, operator error, quality issues, machine overuse, etc. In these cases, preventative maintenance will likely not prevent failure and may waste time and money on unnecessary maintenance.

Table is based on data from studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP) and ARC Consulting

The fact that approximately 80% of failures are non-age-related has major implications for manufacturers trying to decide on a maintenance approach. The traditional preventative-maintenance approach is not likely to address these failures and may even cause failures when improperly done. It is therefore important to consider a more proactive approach, such as condition-based monitoring or predictive maintenance, especially for assets that are critical to the process and/or expensive.

Preventative maintenance and regular inspection may be a good approach for assets more likely to experience age-based failures in Types A, E, and F. These include fans, bearings, and structural components – and in many cases, the cost of condition monitoring or predictive maintenance may not be worth the cost. But for critical components or equipment, such as bearings on an expensive milling machine or transfer line, it may be worthwhile to apply condition monitoring or predictive maintenance.

And when the assets are more likely to experience non-age-related failures (Types B, C, and D), the proactive approaches are better. Many industrial machines and industrial control/motion components fall into this category, and condition monitoring or predictive maintenance can significantly reduce preventative maintenance costs and unplanned failures while improving machine uptime and Overall Equipment Effectiveness.

You can use this information to improve your maintenance operations. Start by considering your maintenance approach(es), especially for your most critical assets:

    • Are they more likely to experience age-related failures or non-age-related failures?
    • Should you change your maintenance approach to be more proactive?
    • What components and indicators should you measure?

We’ll discuss ideas on how to assess your equipment for condition monitoring/predictive maintenance and what you might measure in separate blogs.

* Studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP)