Selecting a Predictive Maintenance system for your manufacturing plant – Guide

Selecting a Predictive Maintenance system for your manufacturing plant - GuideEmbracing digital-only doesn’t rule anymore. At the doorstep of Industry 4.0, enterprises want to look beyond just digitization of processes since that’s mostly covered in Industry 3.0. Today, instant alerts about system failure should be generated in advance and that’s exactly what predictive maintenance is all about.

As per Market Research Future, the global market for predictive solutions is growing enormously to USD 6.3 Billion by 2022. Henceforth, not only will the use of automation go up but a predictive basis like AI will garner greater adoption. What’s interesting is that these solutions work in sync with IoT and collaboratively cleanse data silos, cleanse redundancy and deliver appropriate data inputs.

However, predictive maintenance is new and emerging. Enterprises are still entangled between the compatibility of legacy systems and new technologies. The confusion doesn’t end here. How does the predictive maintenance (PM) system boost the Operation & Management (O & M) proficiency? What budget should be allotted? What solution to go for – Cloud or on-premise? Should you build or buy an off the shelf solution?

If the above bottlenecks sound familiar, the following tips will simplify your thought process.

First things first – Outline the Compliance with the current system

Your hunt for a predictive maintenance system begins with hardware compliance. Since changing the hardware in an industrial setup doesn’t make sense, pick the right software that goes with it. This is because a hardware-independent PM application can be changed or customized seamlessly and secures your investment for the future.

Check for the accurate interfacing of the product with your ERP or any other O & M systems in place. Predictive maintenance is an important function of the Industrial Internet of Things (IoT) and requires all data processes aligned in sync with each other. Pay special attention to compatibility with existing remote monitoring setups you have or with the new remote equipment monitoring system that you are planning to deploy.

So be it performance evaluation feed to the PM system or the insights transmitted to other systems, data connectivity to and from PM will provide you with the needed control.

Advancing into Industry 4.0 scales automation to a whole new level. On-demand PM applications in the cloud not only make you mobile compliant but also abbreviate the cost of critical functions such as performance monitoring, temperature control, vibration monitoring and more.

Understand the data fetching and streaming functionality

While instant insights is one of the reasons to go industrially digital, the ease in data fetching must also be considered. The conventional approach of taking manual readings through a device lacks run time alerts. Although manual reading was taken on a routine basis, the process could capture the status of the equipment at a particular time. Any event of fault just after the reading will be captured in the next session which could happen by EOD or even the next day causing considerable losses.

Predictive maintenance was introduced to resolve this gap. The in-built alert mechanism could broadcast red flag notifications throughout the system landscape including mobile apps. That is why, organizations that have embraced digital monitoring are successful. Therefore, while handpicking PM applications, do not proceed without evaluating the efficiency or latency (if any) in capturing the fault data.

Value to Cost Analysis

You must know what you are paying for. Industries devote enormous budgets for IT systems and yet don’t fully benefit from it. In fact, for most enterprises, IT is turning out to be a sunk cost. What’s worse is the COVID-19 pandemic that has escalated industrial losses to USD 84 Billion. In such critical circumstances, the credibility of all investments is at stake and contemporary applications are no different.

As we usher into the IIoT era, predictive analytics services have to deliver beyond expectations. Nothing less than the ability to predict the equipment life should be agreed upon. Not to miss, the best predictive maintenance application learns the industrial setup and reverts with insights that help the staff perform better. Furthermore, the ability to convert massive production into small yet aligned tasks should judge an application. Before selecting the PM application, make sure you have identified measurable outcomes.

Test the application for different functions

An effective step towards expectation management (discussed above) would be planning the response procedure. Check if the application covers all abnormalities with adequate response procedures including alerts and timing. For example, releasing response actions to different events of failure as per the given parameters in the conditioning procedure.

  • Continue running and collecting data until the scheduled downtime
  • Continue running until a specific production goal is achieved
  • Halt all operations immediately

Another way would be establishing mock failure events and analyzing the application response. Based on the critical assets identified previously, the teams can establish different levels of failure modes towards making predictions. The PM system should be able to predict operational conditions through sensors, predict an abnormality in data patterns and ultimately produce alerts whenever a deviation from the set threshold is identified. As per the results, design an appropriate modeling roadmap and check if the PM application works that way.

Filter the browsing exercise to reliable sources

IoT is a fully grown market place and the web is flooded with service providers. What makes sense is to approach reliable platforms that have done the necessary credibility check of the brands. Not only does it saves time but also introduces you to a whole new ecosystem of possibilities in getting your hands on the most appropriate product. For example, Ioterra has created an online marketplace for IoT products and services of industrial-grade where you can find the best in class engineering partners for your product development or choose an off the shelf solution for your use case with minor customizations. Ioterra does a rigorous background check before onboarding an IoT service partner or solution provider ensuring the reliability that business buyers look for. The consumers get to build a tentative assembly of their IoT ecosystem requirements and potential products matching their requirements on a single platform.

Accuracy of prediction algorithms

After evaluating the PM product upon its usability and scalability metrics, it is equally imperative to judge the underlying algorithm. At the heart of it, PM is all about the degree of accuracy to automate condition monitoring programs. This is essential not only to ensure the successful streaming of all data sets but also to diagnose potential faults in the machine hardware. Therefore, the buyer must enquire about the machine learning algorithms used in the manufacturing program application.

Most PM solutions use the traditional SCADA system that lacks learning complex behavioral patterns. The buyer should take into account the ‘Classification’ and ‘Regression’ metrics in the product. Therefore, the overarching outlook should change from Predictive Maintenance to Predictive Quality Maintenance.

Going forward

The PM ecosystem has to be sustainable for many years. At the same time, it must scale up in compliance with the growing production capacities. Therefore, select a system that can be customized as per the dynamic character of an industrial setup. After all, maximizing operational efficiency is at the crux of all production units.

Author’s Bio: The author of this blog is Abhinav Dubey. Abhinav is an entrepreneur, a strategist and a technologist at heart. With over 10 years of industry experience at the likes of Honeywell and AMD, and over 5 years of experience in IoT with a successful startup exit. Abhinav holds an MBA from the University of Oxford, U.K, and MS from Christ University, Bangalore. He currently serves as the Chief Strategy Officer and General Manager – APAC at Ioterra, Inc.

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