Posted on

Unlocking Industrial Sensor Potential in the IIoT Era

 

In the dynamic landscape of the Industrial Internet of Things (IIoT), one cannot ignore the pivotal role of industrial sensors in revolutionizing manufacturing processes. As we navigate this era of unprecedented connectivity and data-driven decision-making, the true potential of industrial sensors becomes increasingly evident, offering a myriad of benefits to industries worldwide.

Eyes and ears of smart factories

At the heart of this technological renaissance, industrial sensors function as the eyes and ears of smart factories, creating a symphony of data that empowers manufacturers to optimize operations, enhance overall efficiency, and increase profits. The advent of IIoT has amplified the capabilities of these sensors, turning them into indispensable assets for organizations aiming to stay ahead in the competitive industrial landscape.

Imagine a manufacturing floor where every piece of machinery seamlessly communicates with each other, providing real-time data on performance, status, and potential issues. This interconnected ecosystem is made possible by the deployment of advanced industrial sensors and advanced analysis systems. These devices are not merely passive observers; they are the linchpins of a connected industrial infrastructure, facilitating predictive maintenance, reducing downtime, increasing profits, and saving costs.

Real-time data for optimal efficiency

One primary advantage of industrial sensors and systems in the IIoT era is their ability to gather massive volumes of data. This influx of information allows for comprehensive analysis, enabling manufacturers to identify patterns, detect anomalies, and make informed decisions. Predictive analytics powered by industrial sensors transform reactive maintenance into a proactive approach, preventing equipment failures before they occur and ensuring seamless production processes.

Predictive maintenance

Moreover, integrating artificial intelligence (AI) and machine learning (ML) algorithms with industrial sensors takes predictive maintenance to the next level. These intelligent systems can learn from historical data, adapting to changing conditions and continuously improving their accuracy. The result is a finely tuned predictive maintenance strategy that not only minimizes downtime but also extends the lifespan of machinery, optimizing return on investment.

In the IIoT landscape, security is paramount. Industrial sensors, when harnessed correctly, contribute to building robust cybersecurity frameworks. As data flows between devices, encryption protocols and secure communication channels safeguard against potential cyber threats. This initiative-taking approach ensures the integrity of sensitive information and protects against unauthorized access, a crucial aspect in an interconnected industrial ecosystem.

Driving the next industrial revolution

The IIoT era has unshackled the true potential of industrial sensors and systems, transforming them from passive observers to proactive catalysts for innovation. As we continue to explore the boundless possibilities of connectivity and data-driven insights, industrial sensors stand as the unsung heroes, driving the next industrial revolution and ensuring a future where efficiency, sustainability, and competitiveness converge seamlessly on the factory floor.

Posted on

Unlocking the Future of Manufacturing With Smart Sensor Technology

In the present technological age, sensing technology is advancing at an unprecedented pace, transforming the way we monitor the manufacturing process. One of the newest innovations that will reshape various manufacturing and industries is the advent of smart sensor products. These “smart” sensing devices have permeated every aspect of our lives personally, let alone in manufacturing, and offer unparalleled advantages in information, efficiency, convenience, and sustainability. Let’s explore some of the compelling reasons why smart sensors will soon become indispensable in manufacturing and highlight the aspects they will impact.

Beyond single sensing

Smart sensor products are engineered to offer more than just a single sensing function, such as a photoeye sensor detecting the presence of a pallet. They can also detect and respond to various environmental inputs like internal temperature, cycle count, vibration, and even inclination changes. This enables significantly greater insight into a changing manufacturing environment, possibly even prompting the need for human intervention before a failure occurs.

Efficiency, automation, and cost savings

In manufacturing, sensors play a crucial role in improving production processes, reducing waste, and enhancing quality control. But today’s smart sensors can also provide greater efficiency and speed in changes to the manufacturing environment and automate not only the manufacturing process but the detection of changes as well. This increased efficiency and automation not only saves time and resources but also holds the potential for substantial long-term cost savings by minimizing waste.

Real-time insights for informed decisions

Smart sensors can collect and report significant real-time data, providing valuable insights into various phenomena, as mentioned above. In manufacturing, imagine detecting a rise in temperature on the production line that could potentially affect product quality or the efficiency of the manufacturing equipment. Or consider identifying changes in a sensor’s inclination, possibly because the device has come loose or shifts in the machine’s mounting – both of which can negatively affect product quality and productivity, lead to waste, and even unplanned downtime.

Smart sensors and environmental conservation

The ability to collect and analyze precise environment and device performance data empowers manufacturers and industries to make informed decisions, encourage innovation, and significantly improve problem-solving processes.

Smart sensor products can play a pivotal role in environmental conservation efforts. By monitoring conditions like vibration and even inclination, these sensors can detect problems in motors and drive systems that can have a direct impact on energy consumption. Typically, they tend to consume more power to compensate for the impending mechanical failures. By detecting these conditions sooner rather than later, smart sensors can help optimize energy usage in manufacturing industries, contributing to the global push for energy efficiency and reduced carbon emissions.

Safety enhancement

Smart sensor technologies can also bolster safety measures across various systems. In manufacturing, they can detect hazardous conditions like excessive heat buildup and vibration. This enables prompt interventions and helps prevent accidents that could jeopardize safety.

IoT with smart sensors

And finally, smart sensors are at the heart of the Industrial Internet of Things (IIoT) or Industry 4.0, connecting more devices and systems in seamless communication using protocols like IO-Link and Ethernet. This interconnectedness fosters innovation by enabling the development of new, more efficient manufacturing applications and services. For instance, smart sensors in industrial settings help predictive maintenance, which in turn reduces downtime, enhances overall productivity, and bolsters competitiveness. The integration of smart sensors is driving a wave of innovation, transforming ideas into tangible solutions.

Embracing the future: competitive advantage

The adoption of smart sensor products represents a paradigm shift in how we perceive and interact with machines in manufacturing. Their ability to enhance efficiency, improve data analysis, report on, and improve the environment, ensure safety, and foster innovation underscores the significance they can play in the modern manufacturing facility. As we continue to explore the boundless possibilities of interacting technology, embracing smart sensor products is not just a choice, but a competitive advantage. By integrating these intelligent devices into our machines and industries, we are paving the way for a future that is more productive, efficient, environmentally sustainable, and more interconnected. This marks another transformative leap toward a smarter and more interconnected manufacturing world.

Posted on

Enhancing Manufacturing Efficiency: OEE Measurement Through Sensors

Optimizing operational efficiency in manufacturing is crucial for businesses seeking to stay competitive. One powerful tool for measuring and enhancing manufacturing performance is overall equipment effectiveness (OEE). By leveraging sensor technology, manufacturers can gain valuable insights into their production processes, enabling them to identify areas for improvement, reduce downtime, and boost overall productivity.

What is OEE?

OEE is a metric for measuring the efficiency and productivity of a manufacturing process, including three key factors: availability, performance, and quality. Availability measures the percentage of time that equipment is available for production, while performance measures the speed at which the equipment runs. Quality measures the rate of products that meet the required quality standards. Combining these factors, OEE provides a comprehensive view of how well a manufacturing process performs and can help determine the need for improvements.

Sensors: the building blocks of OEE measurement

Sensors play an important role in helping manufacturers determine the effective use of equipment. Following are some key metrics that sensors can track:

    • Machine health monitoring: Sensors can continuously monitor the condition of machines, detecting anomalies and potential breakdowns before they escalate. Predictive maintenance, facilitated by sensor data, helps reduce unplanned downtime, increasing equipment availability.
    • Production tracking: Sensors can track production rates and cycle times, comparing them to target rates. This data empowers businesses to assess equipment performance and identify bottlenecks that hinder optimal efficiency.
    • Quality control: Implementing sensors for real-time quality inspection ensures the prompt identification and removal of defective products from the production line, enhancing the overall quality factor in the OEE calculation.
    • Downtime analysis: Sensors can log and categorize downtime events, providing valuable insights into the root causes of inefficiencies. With this knowledge, manufacturers can implement targeted improvements to reduce downtime and enhance availability.
    • Energy efficiency: Some advanced sensors can monitor energy consumption, allowing businesses to optimize energy usage and contribute to sustainability efforts.

Integrating sensors and OEE measurement

The integration of sensors into the manufacturing process might seem daunting, but it offers numerous benefits that far outweigh the initial investment:

    • Real-time insights: Sensors provide real-time data, enabling manufacturers to monitor performance, quality, and availability metrics continuously. This empowers businesses to take immediate action when issues arise, minimizing the impact on production.
    • Data-driven decision-making: By analyzing sensor-generated data, manufacturers can make informed decisions about process improvements, equipment upgrades, and workforce optimization to enhance OEE.
    • Continuous improvement: OEE measurement with sensors fosters a culture of continuous improvement within the organization. Regularly reviewing OEE data and setting improvement goals drives teams to work collaboratively towards boosting overall efficiency.
    • Increased competitiveness: Manufacturers leveraging sensor-driven OEE measurement gain a competitive edge by optimizing productivity, minimizing downtime, and producing high-quality products consistently.

Measuring OEE using sensors is crucial to achieving operational excellence in modern manufacturing. Using real-time sensor data, manufacturers can identify areas for improvement, reduce waste, and boost productivity. Integrating OEE and sensor technology streamlines production processes and encourages continuous improvement. This approach helps manufacturers stay ahead in the ever-changing industrial landscape.

Read the Automation Insights blog Improving Overall Equipment Effectiveness to learn about the focus areas for winning the biggest improvements in OEE.

Posted on

Improving Overall Equipment Effectiveness

Overall equipment effectiveness (OEE) is a critical metric for measuring the efficiency of manufacturing operations. It considers three factors – availability, performance, and quality – to determine the effective use of equipment.

Where do we focus to win the biggest improvements?

To improve OEE, it’s important to focus on these five key areas:

    1. Equipment maintenance: Ensuring equipment is well-maintained is critical to achieving high OEE. Regular inspections, preventive maintenance or, even better, “predictive maintenance,” and prompt repairs can help minimize downtime from unexpected breakdowns. Condition monitoring sensors and the data they generate can predict where failures may to occur so action can be taken to avoid such downtimes.
    2. Production planning: Effective production planning can help optimize production schedules, minimize set-up time, and reduce changeover time, as well as help increase equipment utilization and reduce downtime. Software solutions are available that provide operators with guidance and optimize changeovers between different set-ups or formats.
    3. Process optimization: Analyzing and optimizing production processes can help identify bottlenecks, reduce waste, and improve overall efficiency. This can involve implementing process improvements, such as reducing cycle times or optimizing material flow.
    4. Workforce training: A well-trained workforce can help minimize errors, reduce downtime, and improve overall quality. Providing employees with the necessary skills and training can also help increase productivity and equipment utilization. Operator guidance, including digital work instruction, which is available in a digital format, is increasingly familiar to the newer members of the workforce.
    5. Data analysis: Collecting and analyzing OEE and downtime data, and other key metrics can help identify areas for improvement and guide decision-making on where to focus. Implementing real-time monitoring and analysis can help detect issues early, well before a failure, and thus, minimize the impact on production.

By focusing on and ranking the areas outlined above, manufacturers can improve overall equipment effectiveness and achieve greater efficiency, productivity, and, most importantly, profitability.

Posted on

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.

Posted on

Automation Insights: Top Blogs From 2022

It’s an understatement to say 2022 had its challenges. But looking back at the supply chain disruptions, inflation, and other trials threatening success in many industries, including manufacturing, there were practical insights we can benefit from as we dive into 2023. Below are the most popular blogs from last year’s Automation Insights site.

    1. Evolution of Pneumatic Cylinder Sensors

Top 2022 Automation Insights BlogsToday’s pneumatic cylinders are compact, reliable, and cost-effective prime movers for automated equipment. They’re used in many industrial applications, such as machinery, material handling, assembly, robotics, and medical. One challenge facing OEMs, integrators, and end users is how to detect reliably whether the cylinder is fully extended, retracted, or positioned somewhere in between before allowing machine movement.

Read more.

    1. Series: Condition Monitoring & Predictive Maintenance 

Top 2022 Automation Insights BlogsBy analyzing which symptoms of failure are likely to appear in the predictive domain for a given piece of equipment, you can determine which failure indicators to prioritize in your own condition monitoring and predictive maintenance discussions.

Read the series, including the following blogs:

    1. Know Your RFID Frequency Basics

Top 2022 Automation Insights BlogsIn 2008 I purchased my first toll road RFID transponder, letting me drive through and pay my toll without stopping at a booth. This was my first real-life exposure to RFID, and it was magical. Back then, all I knew was that RFID stood for “radio frequency identification” and that it exchanged data between a transmitter and receiver using radio waves. That’s enough for a highway driver, but you’ll need more information to use RFID in an industrial automation setting. So here are some basics on what makes up an RFID system and the uses of different radio frequencies.

Read more.

    1. IO-Link Event Data: How Sensors Tell You How They’re Doing

Top 2022 Automation Insights BlogsI have been working with IO-Link for more than 10 years, so I’ve heard lots of questions about how it works. One line of questions I hear from customers is about the operating condition of sensors. “I wish I knew when the IO-Link device loses output power,” or, “I wish my IO-Link photoelectric sensor would let me know when the lens is dirty.” The good news is that it does give you this information by sending Event Data. That’s a type of data that is usually not a focus of users, although it is available in JSON format from the REST API.

Read more.

    1. Converting Analog Signals to Digital for Improved Performance

Top 2022 Automation Insights BlogsWe live in an analog world, where we experience temperatures, pressures, sounds, colors, etc., in seemingly infinite values. There are infinite temperature values between 70-71 degrees, for example, and an infinite number of pressure values between 50-51 psi.

Read more.

We appreciate your dedication to Automation Insights in 2022 and look forward to growth and innovation in 2023.

Posted on

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)

Posted on

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:

Posted on

How IO-Link Sensors With Condition Monitoring Features Work With PLCs

As manufacturers continually look for ways to maximize productivity and eliminate waste, automation sensors are taking on a new role in the plant. Once, sensors were used only to provide detection or measurement data so the PLC could process it and run the machine. Today, sensors with IO-Link measure environmental conditions like temperature, humidity, ambient pressure, vibration, inclination, operating hours, and signal strength. By setting alarm thresholds, it’s possible to program the PLC to use the resulting condition monitoring data to keep machines running smoothly.

Real-time data for real-time response

A sensor with condition monitoring features allows a PLC to use real-time data with the same speed it uses a sensor’s primary process data. This typically requires setting an alarm threshold at the sensor and a response to those alarms at the PLC.

When a vibration threshold is set up on the sensor and vibration occurs, for example, the PLC can alert the machine operator to quickly check the area, or even stop the machine, to look for a product jam, incorrect part, or whatever may be causing the vibration. By reacting to the alarm immediately, workers can reduce product waste and scrap.

Inclination feedback can provide diagnostics in troubleshooting. Suppose a sensor gets bumped and no longer detects its target, for example. The inclination alarm set in the sensor will indicate after a certain degree of movement that the sensor will no longer detect the part. The inclination readout can also help realign the sensor to the correct position.

Detection of other environmental factors, including humidity and higher-than-normal internal temperatures, can also be set, providing feedback on issues such as the unwanted presence of water or the machine running hotter than normal. Knowing these things in real-time can stop the PLC from running, preventing the breakdown of other critical machine components, such as motors and gearboxes.

These alarm bits can come from the sensors individually or combined together inside the sensor. Simple logic, like OR and AND statements, can be set on the sensor in the case of vibration OR inclination OR temperature alarm OR humidity, output a discrete signal to pin 2 of the sensors. Then pin 2 can be fed back through the same sensor cable as a discrete alarm signal to the PLC. A single bit showing when an alarm occurs can alert the operator to look into the alarm condition before running the machine. Otherwise, a simple ladder rung can be added in the PLC to look at a single discrete alarm bit and put the machine into a safe mode if conditions require it.

In a way, the sensor monitors itself for environmental conditions and alerts the PLC when necessary. The PLC does not need to create extra logic to monitor the different variables.

Other critical data points, such as operating hours, boot cycle counters, and current and voltage consumption, can help establish a preventative and predictive maintenance schedule. These data sets are available internally on the sensors and can be read out to help develop maintenance schedules and cut down on surprise downtimes.

Beyond the immediate benefits of the data, it can be analyzed and trended over time to see the best use cases of each. Just as a PLC shouldn’t be monitoring each alarm condition individually, this data must not be gathered in the PLC, as there is typically only a limited amount of memory, and the job of the PLC is to control the machines.

This is where the IT world of high-level supervision of machines and processes comes into play. Part two of my blog will explore how to integrate this sensor data into the IT level for use alongside the PLC.