In process industries today, all organisations, regardless of their level of smart machine sophistication, are interested in reducing downtime, and in shortening their maintenance department Mean Time to Repair (MTTR). According to the U.S. Department of Energy, predictive maintenance (the ability to repair a plant asset just before it fails) is highly cost effective, saving roughly eight percent to 12 percent over preventive maintenance (which is regularly scheduled, calendar-based maintenance), and up to 40 percent over reactive maintenance (performing no maintenance on operating equipment until it unexpectedly breaks).
However, organisations working with an installed base of older machines are limited in their ability to achieve the benefits of predictive maintenance. They lack the means to capture shop floor data from their process machines and instruments. Plants that lack this connectivity are, for the most part, running in manual mode, and their potential to compete erodes over time as frequent instances of downtime reduce productivity and efficiency.
We at Adarsha Control and Automation, an experienced industrial automation systems integrator and EcoXpert, and recent winner of the Schneider Electric 2020 Alliance Partner Award, help process industry organisations with both their predictive maintenance planning and execution. We are experts at enabling both existing and new smart machines to capture data. We also deliver customised dashboards that analyse and present machine performance data in formats that enable quick and accurate maintenance decisions. Here are the steps we use to ensure our customers maximise the return on their predictive maintenance investments.
Smart Machine Predictive Maintenance Benefits
- Equip machinery with sensors and updated automation controllers – The ability to capture smart machine data is step 1, and sensors are the perfect inexpensive devices for measuring important performance indicators such as heat and vibration. Within the realm of electrical equipment, for instance, rising heat inside of electrical cabinets is often a sign of a loose connection or of eroding insulation. If left unchecked, the situation could lead to a short circuit or even an arc flash incident which will, in turn, result in protracted downtime. Recognising that a heat anomaly exists allows maintenance personnel to perform a fix ahead of time, with minimal disruption to operations, before an incident of costly unanticipated downtime occurs.
- Establish machinery behavioural parameters and thresholds – As a systems integrator, we work in conjunction with plant engineers and operators to establish acceptable performance parameters (such as temperature ranges) for the electrical or mechanical equipment in question. We base our recommendations on industry standards and factor in research information such as motor and pump efficiencies. We then transfer that information to our software engines and perform analytics based on those parameters. As a final step, we set up the system to issue alarms when parameters are exceeded.
- Develop dashboards that serve as advisors – We use flowcharts and software tools to create custom dashboards for our customers. These dashboards are designed to gather the new sources of available information (generated from the sensors) and provide deeper insights regarding plant and asset performance. The dashboards are also used to plan for the proper sequence of maintenance interventions.
Selecting the Right Tools Ensures Peace of Mind
Every minute of downtime results in both a financial and competitive cost. Therefore, making the right choices when developing a predictive maintenance strategy is key.
Schneider Electric supports our effort to modernise process industry maintenance approaches by providing digital tools like EcoStruxure Machine Advisor and EcoStruxure Augmented Operator Advisor that help capture and analyse data in a much more affordable and less intrusive manner. The predictive analytics integrated into these products makes it easy for maintenance staff members to identify anomalies and provides notification of abnormal conditions.
Decisions influenced by predictive controls need to be made fast enough to positively impact the operation of a process. Fast and accurate decisions will result in true asset performance control which will then lead to optimal enterprise performance.
Article by Sourav Sinha, GM for Sales and head of the Eastern Region Business Unit, Adarsha Control & Automation Pvt Limited.
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