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Automation, Networking and Sensors in Intralogistics

The intralogistics industry has made significant investments in automation since 2020. The boom in ecommerce, accelerated by the pandemic, pressured online retailers to improve their warehouse operations through automation. Traditional manufacturers and non-ecommerce (B2B) firms have been slower in automating their intralogistics operations, and penetration is still relatively low. This is rapidly changing, driven by market factors such as labor issues, product individualization, supply chain challenges, higher efficiency/productivity/quality, faster delivery, and wider adoption of ecommerce by B2B firms.

The deeper penetration of automation in intralogistics means that the applications are now using or adapting many of the same technologies and smart manufacturing processes employed in traditional manufacturing: robotics, PLCs and motion controllers, industrial networks, sensors, motors and drives, RFID/code reading, vision systems, human-machine interfaces, automation software, IIoT/Industry 4.0, and more. This blog focuses on the use of sensors, networking, and RFID/code reading in common intralogistics processes:

    • Conveying and transporting
    • Storage and retrieval
    • Sorting and picking

Within these areas, there are several key applications. I’ll go into more detail on solutions for each of them:

    • Object detection
    • Controls architecture
    • Traceability
    • Condition monitoring

Object detection: Photoelectric, inductive, ultrasonic, and capacitive sensors are used to reliably detect objects on conveyors, container fill levels, and object presence, position, shape, color, distance, or thickness. Photoelectric sensors are often used to detect bottles, totes, or material on conveyors or to detect items in racks or on transporters. Inductive sensors can detect metal objects on conveyors or in racks, but also the position of parts of the equipment to verify position, alignment, or proper operation. While photoelectric or inductive sensors can also detect objects for picking and sorting applications, vision systems are often used when robots are involved in the process.

Controls architecture: Connecting sensors and devices to the control system can be time-consuming and complicated, involving long cables, many terminations, and difficult troubleshooting. The automation industry, therefore, uses industrial networking to simplify controls architectures. It is an especially interesting and cost-effective approach for intralogistics because the facilities are often large, with long distances and many sensors. Network blocks and hubs using technologies such as IO-Link make it easy and inexpensive to connect many sensors using common M12 or M8 cables. IO-Link not only gathers standard process data but also provides diagnostic/event and parameter data. This simplifies detecting the individual device status and troubleshooting mistakes in wiring or broken sensors.  When implementing automation, especially for large-scale conveying or storage and retrieval systems, companies typically apply a networked controls architecture across most intralogistics processes.

Traceability: Tracking the movement of goods through a facility is a critical part of the intralogistics process. The most used technologies are RFID and code reading; selection depends on the application. RFID is generally available in low (LF), high (HF), and ultra-high (UHF) frequencies. LF and HF RFID are good for short-range part tracking and production control where data needs to be read/written to a single tag at a time (for example, items on a conveyor). UHF RFID systems are better for longer distance detection of multiple tags (for example, tracking pallets through a facility). Coder readers are popular in intralogistics facilities because bar codes are common, simple, and easy to use. Reader technology has evolved to address past challenges such as reading multiple codes at once, imprecise code location, and code type variation. In some cases, companies use code reading for positioning storage systems or navigating AGVs.

Condition monitoring: Reducing unplanned downtime and improving Overall Equipment Effectiveness (OEE) are focus topics in intralogistics automation, and condition monitoring offers a solution to these challenges. A wide variety of sensors are available to detect vibration, temperature, pressure, flow, and humidity to help monitor equipment conditions. This sensor data can be easily gathered through the controls architecture or “add-on” data gateways, with IO-Link offering a wide variety of sensor and gateway choices. The most common intralogistics condition monitoring applications involve motion (motors, gearboxes, bearings, shafts, pumps, fans) for conveyors, storage/retrieval, and transport systems.

The use of automation in intralogistics will continue to grow rapidly as both ecommerce firms and traditional manufacturers seek to optimize their warehouse, conveying, and picking/sorting operations in response to industry and societal trends. These companies are realizing that worker shortages, faster delivery, improved quality, higher efficiency, mass customization, and supply chain issues are best addressed by automation.

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Effective Condition Monitoring: A Practical Guide for Maintenance Technicians

In my decade of experience supporting sales organizations as an application and product specialist across various industries, I’ve observed a common trend among customers: The majority tend to focus solely on vibration when it comes to condition monitoring for their assets. While vibration analysis is crucial for detecting mechanical component issues, it’s essential to recognize that it’s not the only method available. In this blog. I present a straightforward four-step process to guide maintenance technicians in implementing effective condition monitoring beyond vibration analysis.

Step 1: Identify critical assets. Begin by finding the key assets or machines in your plant that can significantly affect unplanned downtime if they were to fail. Understanding the critical components if the first step in establishing an effective condition-monitoring strategy.

Step 2: Determine failure modes. Once you know the critical assets, figure out the potential failure modes of each machine. Knowing how a machine might fail enables you to choose the most relevant sensing technology for monitoring specific parameters contributing to potential failures.

Step 3: Choose sensing technology. Consider the failure modes you found in Step 2 and choose the appropriate sensing technology to monitor the parameters relevant to each machine. This step involves thinking beyond vibration analysis and exploring solutions that address diverse factors such as:

    • Pressure
    • Flow
    • Level
    • Position
    • Load
    • Temperature
    • Humidity
    • Viscosity
    • Impurity/contamination

Step 4: Implement a monitoring solution. Before implementing the chosen solution across the entire plant, conduct a pilot test on one machine. This step allows you to evaluate the viability of the monitoring solution and ensures it aligns with the application needs. It’s a practical approach to minimize risks and optimize the effectiveness of your condition monitoring strategy.

Condition monitoring diversity

To emphasize the diversity of condition monitoring, consider scenarios where the vibration analysis falls short. For instance:

    • Hydraulic Power Unit: Use a level sensor to monitor oil levels in the tank, preventing failures due to leaks.
    • Air Compressor: Employ a pressure transducer and flow sensor to monitor air pressure and flow, addressing issues with a worn-out air pump.
    • Electrical Cabinets: Protect critical electrical components by using ambient temperature and humidity sensors to detect elevated temperatures or moisture ingress.

In conclusion, break free from the confines of relying solely on vibration analysis. By following this four-step process, maintenance technicians can tailor condition monitoring strategies to the unique needs of their plants. Explore diverse sensing technologies, implement pilot solutions, and ensure you are monitoring your assets comprehensively to mitigate risks and optimize performance.

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Automation Insights: Top 10 Blogs From 2023

In 2023, the industrial automation sector experienced significant advancements and transformative trends, shaping the landscape of manufacturing and production processes. Listed below are our top 10 blogs highlighting some of these advancements, from streamlined changeover processes using RFID to machine safety levels determined through risk assessments and a proactive approach to unplanned downtime using condition monitoring. Other blogs explored UHF RFID considerations, communication protocol analysis, camera selection guidance for engineers, machine safety practices emphasis, and discussions on IO-Link and MQTT benefits for automation projects.

    1. Using RFID Technology for Rapid Changeover

In today’s tight economy, marked by high inflation and supply chain issues, the need to enhance product flexibility has become increasingly important. Most manufacturing lines these days are set up to run multiple work orders of the same product type based on specific requirements. The goods produced at the manufacturer line are still the same, but the package size can change. The raw materials that start the process might be the same, but other component parts and tools on the machine that help with the different packaging sizes must be replaced. The process of converting one product line or machine to another is known as changeover. This blog explores how Radio Frequency Identification (RFID) technology can revolutionize changeover by eliminating manual verification and adjustments.

Read more.

    1. Understanding Machine Safety: The Power of Risk Assessments

My last blog post was about machine safety with a focus on the different categories and performance levels of machine safety circuits. But I just briefly touched on how to determine these levels. By default, we could design all equipment with the highest-level category and performance levels of safety with an abundance of caution, but this approach could be extremely expensive and not the most efficient.

Read more.

    1. Getting Started With Condition Monitoring

Unplanned downtime is consistently identified as one of the top manufacturing issues. Condition monitoring can offer a fairly simple way to start addressing this issue and helps users become more proactive in addressing and preventing impending failures of critical equipment by using data to anticipate problems.

Read more.

    1. Sensing Ferrous and Non-Ferrous Metals: Enhancing Material Differentiation

Detecting metallic (ferrous) objects is a common application in many industries, including manufacturing, automotive, and aerospace. Inductive sensors are a popular choice for detecting metallic objects because they are reliable, durable, and cost-effective. Detecting a metallic object, however, is not always as simple as it seems, especially if you need to differentiate between two metallic objects. In such cases, it is crucial to understand the properties of the metals you are trying to detect, including whether they are ferrous or non-ferrous.

Read more.

    1. Considerations When Picking UHF RFID

If you’ve attempted to implement an ultra-high frequency (UHF) RFID system into your facility, you might have run into some headaches in the process of getting things to work properly. If you are looking to implement UHF RFID, but haven’t had the chance to set things up yet, then this blog might be beneficial to keep in mind during the process.

Read more.

    1. Comparing IO-Link and Modbus Protocols in Industrial Automation

In the realm of industrial automation, the seamless exchange of data between sensors, actuators, and control systems is critical for optimizing performance, increasing efficiency, and enabling advanced functionalities. Two widely used communication protocols, IO-Link and Modbus, have emerged to facilitate this data exchange. In this blog, I’ll analyze the characteristics, strengths, and weaknesses of both protocols to help you choose the right communication standard for your industrial application.

Read more.

    1. Exploring Industrial Cameras: A Guide for Engineers in Life Sciences, Semiconductors, and Automotive Fields 

In the bustling landscape of industrial camera offerings, discerning the parameters that genuinely define a camera’s worth can be a daunting task. This article serves as a compass, steering you through six fundamental properties that should illuminate your path when selecting an industrial camera. While the first three aspects play a pivotal role in aligning with your camera needs, the latter three hold significance if your requirements lean towards unique settings, external conditions, or challenging light environments.

Read more.

    1. Focusing on Machine Safety

Machine safety refers to the measures taken to ensure the safety of operators, workers, and other individuals who may encounter or work in the vicinity of machinery. Safety categories and performance levels are two important concepts to evaluate and design safety systems for machines. A risk assessment is a process to identify, evaluate, and prioritize potential hazards and risks associated with a particular activity, process, or system. The goal of a risk assessment is to identify potential hazards and risks and to take steps to prevent or mitigate those risks. The hierarchy of controls can determine the best way to mitigate or eliminate risk. We can use this hierarchy, including elimination, substitution, engineering, and administrative controls, and personal protective equipment (PPE), to properly mitigate risk. Our focus here is on engineering controls and how they relate to categories and performance levels.

Read more.

    1. Why Choose an IO-Link Ecosystem for Your Next Automation Project?

By now we’ve all heard of IO-Link, the device-level communication protocol that seems magical. Often referred to as the “USB of industrial automation,” IO-Link is a universal, open, and bi-directional communication technology that enables plug-and-play device replacement, dynamic device configuration, centralized device management, remote parameter setting, device level diagnostics, and uses existing sensor cabling as part of the IEC standard accepted worldwide.

Read more.

    1. 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.

Read more.

We appreciate your dedication to Automation Insights in 2023 and look forward to growth and innovation in 2024.

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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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Getting Started With Condition Monitoring

What is condition monitoring?

Unplanned downtime is consistently identified as one of the top manufacturing issues. Condition monitoring can offer a fairly simple way to start addressing this issue and helps users become more proactive in addressing and preventing impending failures of critical equipment by using data to anticipate problems.

There are four common maintenance approaches: reactive, preventative, condition-based, and predictive. Each has different cost-benefit tradeoffs, and it may be appropriate to use multiple approaches depending on the range of equipment in a facility. In general, the reactive and preventative approaches have significant drawbacks when used on critical equipment and when unplanned downtime is a major concern.

Condition-based monitoring and predictive maintenance (which uses condition-based sensors, tools, and data) offer approaches that can proactively warn of impending failure and are especially relevant to important equipment.

    • Reactive: “Run until it breaks” might be used on non-critical, low-value assets, but is highly risky to apply to important components, where costs of repair and costs of downtime are high.
    • Preventative: “Maintain at regular intervals, whether the asset needs it or not” might be appropriate when failures are age-related, but it may be that costly maintenance is being done on assets that do not need it.
    • Condition-based: “Monitor limits on relevant indicators” can address failures regardless of whether they are age-based or random and monitors changes in one or more potential failure indicators, such as vibration, temperature, current/voltage, pressure, etc.
    • Predictive maintenance and analysis: Attempt to learn from machine performance (condition-based data) to predict failure.

Condition monitoring provides warnings about faults and makes it possible to schedule repairs without unplanned downtime and lost production. It focuses on using sensors to monitor the status and health of machines, plants, or individual components (bearings, motors, fans, etc.) and then transmitting this data to control and/or supervisory systems for analysis and action. Continuous condition monitoring aims to detect changes and anomalies and can help customers record long-term trends and statistical evaluation – an entry point into predictive maintenance and predictive analytics.

How condition monitoring works

Typically, as a failure progresses, different indicators emerge (vibration, temperature, change in pressure & flow, etc.), and monitoring these can allow a more proactive approach than reactive or predictive maintenance. The Potential-Functional (“P-F”) Curve provides an example of the lifecycle of a failure:

Warning and alarm limits for the selected indicator(s) are set and when the limits are reached action can be taken. The limits can be set based on recommendations from the equipment manufacturer, ISO 10816-3 guidelines, or test data gathered from the machine. Over time, the data gathered can be analyzed to modify the limits and can be used as the basis for predictive maintenance and analysis.

When an alarm is triggered the maintenance staff can investigate and address the issue in a proactive manner – whether a simple task such as lubrication or minor adjustment, or a more critical part replacement – generally with time to schedule the activity during a planned downtime, rather than in the middle of production.

How to get started

We suggest you start with a small pilot system:

    • Perhaps use a demo system, portable, or temporary tool.
    • Set the initial alarm/warning limits based on ISO standards, manufacturer recommendations, or experience with similar machines.
    • Gather data and look for insights.
    • Modify limits based on data and consider using predictive maintenance software/tools for deeper analysis.
    • Create buy-in with maintenance teams and the leadership team.
    • Document the positive impacts of the changes and discuss them often.
    • Grow the system over time.

Once you are ready to expand, an article in Control Engineering magazine provides advice on a process we endorse, including:

    1. Conduct a criticality analysis: Which assets are most critical and have the most impact if they fail?
    2. Identify probable failures the asset will experience: How has it failed in the past? What has happened to similar equipment? Does the manufacturer have recommendations?
    3. Decide on the technology best suited to detect each failure mode: Do you need to monitor a device, machine, or complete facility? What are the most appropriate indicators and the sensors to detect them? Do you need continuous or one-time monitoring? Where is the data needed and what is the best way to transfer it?
    4. Trend and analyze the data to plan and execute maintenance actions at the most advantageous times: How will you visualize the data? Do you want to use software to do analysis for you? Are there obvious trends and conclusions to be made?

Getting started with condition monitoring can seem challenging and complicated. By starting small you can learn what does and doesn’t work and take a more proactive approach to maintenance as you spread condition monitoring throughout your facility.

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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.

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Improving Conveyor Performance: The Value of Condition Monitoring

In the realm of industrial operations, even the smallest improvements can yield substantial gains. This rings especially true when considering the often overlooked yet indispensable component of many manufacturing operations: conveyors. While these mechanical workhorses silently go about their tasks, incorporating a touch of innovation in the form of condition monitoring sensors can yield significant payoffs. In this blog, I delve into why integrating condition monitoring into your conveyance systems isn’t just a good idea – it’s a savvy investment in efficiency, reliability, and overall peace of mind.

The case for condition monitoring

Here are five compelling reasons why condition monitoring is an essential addition to your conveyor systems:

    1. Reducing unplanned downtime: The ability to keep an eye on the health and performance of critical conveyor components empowers you to detect potential issues before they snowball into disruptive downtime events. Proactive, even predictive, maintenance becomes the name of the game, minimizing the risk of unscheduled stoppages.
    2. Enhancing reliability: Early issue identification leads to fewer instances of system failures and breakdowns. By fostering a proactive maintenance approach, condition monitoring bolsters the overall reliability of your conveyor system, offering a buffer against unexpected interruptions.
    3. Elevating safety standards: Safety should never be an afterthought. Condition monitoring serves as a vigilant sentinel, flagging potential safety concerns within the conveyor system. For instance, it can detect abnormal vibrations in motors or gearboxes, thereby averting catastrophic failures that might jeopardize personnel safety or equipment integrity.
    4. Trimming maintenance costs: The ability to time maintenance activities optimally, thanks to condition monitoring, can translate into substantial cost savings. Instead of waiting for a failure to necessitate urgent fixes, you can schedule maintenance when it’s most cost-effective, avoiding pricier last-minute solutions and expedited freight expenses.
    5. Prolonging equipment lifespan: Condition monitoring, by tracking the condition of vital components, helps you pinpoint or predict exactly when maintenance or repairs are due. This precision not only extends the lifespan of your equipment but also curbs the need for costly replacements or frantic damage control.

Adding condition monitoring to your conveyor systems isn’t just about immediate gains in efficiency and reliability – it’s an investment that can significantly reduce maintenance costs and stretch the lifespan of your equipment. Why wait? Make the investment today and unlock the financial benefits and peace of mind that come with the utilization of condition monitoring across your conveyor systems.

Click here to learn more about condition monitoring.

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Driving Efficiency and Reliability in Automotive Manufacturing

In the days of Henry Ford – when you could get a car in any color as long it was black – the assembly line involved grabbing a part and putting it on the car. Today, there are literally thousands of iterations of car options, drastically increasing the need for tracking and traceability of all parts that go into the cars. How do you ensure that the components going into vehicles are the correct ones?

Limitations of traditional barcode stickers

The answer is ever-evolving. At first, automotive companies were printing off one-dimensional barcodes on stickers – a time-consuming, labor-intensive, and often wasteful process. It was necessary for an individual to print a stack of stickers hoping that they were correct and in the right order, manually put them on the parts, and hope they wouldn’t fall off. Unfortunately, many times they did fall off, leaving the operators without a way to track the parts. And once the part hit the assembly line, the operator had to manually scan the barcode, which typically took six to 10 seconds.

The power of optical identification sensors

Modern automotive companies are automating this process with sensors for optical identification. They can reliably and precisely read both 1D and 2D bar codes. This two-step process includes:

    1. Using lasers (CO2 for plastic or Fiber for metal), a Direct Part Mark (DPM) is permanently etched onto the component. This DPM remains readable throughout the component’s lifespan.
    2. Once marked, a nest is created on the component, equipped with two to four cameras. These cameras capture visible 2D data matrices or 1D sticker barcodes from up to 600mm away. All data is transmitted via IO-Link to the PLC. This process eliminates scanning errors and reduces scrap.

Advanced condition monitoring for quality and efficiency

In addition to code reading functions, advanced condition monitoring capabilities have become an essential part of ensuring quality and efficiency in automotive manufacturing. These capabilities enable the continuous monitoring of various parameters related to the components and their operational conditions. Sensors equipped with advanced condition monitoring features, such as temperature sensors, vibration sensors, humidity sensors, inclination sensors, signal quality sensors, and operating time sensors, are deployed alongside the code reading sensors.

Overall, the combination of code reading sensors and advanced condition monitoring capabilities ensures not only the correct identification and traceability of components but also enhances overall quality control, reduces downtime, minimizes scrap, and improves the reliability and performance of the final products.

Click here for more information on optical code readers with IO-Link and condition monitoring.

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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.