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Predicting Failure before it Happens

Thomas Schardt, Senior Director of IoT, Nidec Motor Corporation [TYO:6594] and Pranesh Rao, Senior Product Manager of IoT, Nidec Motor Corporation [TYO:6594]
Thomas Schardt, Senior Director of IoT, Nidec Motor Corporation [TYO:6594]

Thomas Schardt, Senior Director of IoT, Nidec Motor Corporation [TYO:6594]

Somewhere in North America, a compressor used in a manufacturing process will stop working today. Whether it is due to mechanical failure or something else, the result will be largely the same: production will be disrupted, workers will be idled and emergency repair funds will be spent.

The good news is, events like these may not be typical for much longer.

Thanks to a precipitous drop in the cost of sensors, microchips, other electronic components, and cloud data storage, along with powerful new predictive analytics, it is now far easier—and less expensive—than ever to avoid production line failures. New stand-alone predictive maintenance solutions can now be applied to critical processes that had historically been cost-prohibitive.

What Difference a Sensor Makes

Monitoring critical production line assets, of course, is not new. SCADA systems have long been used to monitor manufacturing processes. Companies use them, for example, to track asset uptime.

But monitoring processes like these aren’t particularly effective at signaling to operators when equipment failure might be imminent, or identify the root cause of a problem. Using the sensors on motors and other equipment, it is now possible to collect continuous, real-time data that suggests when a device is approaching an unexpected shutdown or experiencing conditions that might negatively impact process performance. Depending on the application, that data can include everything from vibration and temperature, current and voltage, to flow and pressure monitoring.

  New remote asset condition monitoring and predictive maintenance solutions reduce operational costs and extend equipment service life  

Consider, for example, what happened recently at one of our key suppliers’ manufacturing plant. The plant operates several production lines, two of which have been equipped with an asset condition monitoring system for remotely monitoring motor vibration.

Shortly after installation, one production line’s vibration levels began exceeding set parameters. Analysis of the vibration data indicated that a motor bearing was on the verge of failure. Further review suggested that the bearing could function for a few more days—enough time to secure a replacement part that could be installed during a controlled shutdown.

Three days later, the plant did just that. Production was moved to another line while the bearing was replaced at a fraction of the cost that would have been incurred had the equipment failed unexpectedly.

Consider how the lost time, production and costs would have piled up had the production line not been continuously monitored. In that case, the motor would have failed and production would have come to an immediate halt while a maintenance team worked to identify the problem. In addition to diagnostic and repair costs, the company would have faced rush charges, lost production and wages—not to mention the potential customer dissatisfaction brought on by delayed product shipment. The result of an undetected bearing failure could have easily been 10 times the amount actually spent.

An Economical Alternative to Route-based MonitoringPranesh Rao, Senior Product Manager of IoT, Nidec Motor Corporation [TYO:6594]

Remote asset condition monitoring systems save time and money in other ways as well. Many plants, for example, still monitor critical equipment by contracting third-party specialists who survey conditions along a specific route. The data they collect can later be downloaded and compared to previous reports to identify signs of deterioration. But because the data is collected only intermittently—often only quarterly— it can be difficult, if not impossible, to pinpoint when or how a specific problem is triggered. Therefore, its value can be limited.

Consider one of our manufacturing plants, where we operate several die cast ovens and other critical manufacturing equipment. We currently pay an external contractor approximately $3,000 each year to conduct quarterly route-based monitoring of critical equipment in this plant. However, the company will be switching to a remote monitoring system at a one-time capital cost of approximately $4,000. In subsequent years, annual data storage and management cost will total less than $1,000. Data will now be available in real-time and accessible at anytime from anywhere. The external specialist’s extensive knowledge in vibration analysis may still be used for data evaluation, but in a much more focused and effective manner.

Not only are overall remote monitoring system costs dramatically lower than the route-based approach, the remote monitoring system delivers more actionable real-time information. For example: an alarm now alerts users if smelting oven temperature exceeds or drops below preset value, or vibration levels are outside an acceptable range.

Predictive Analytics Add Value

This advanced form of monitoring gains even more value when predictive analytics are added to the equation. Using algorithms, the data collected through monitoring can be modeled to identify patterns that help users not only intervene on imminent breakdowns, but also predict future events.

A monitoring system, for example, can be designed to alert users if operating conditions change—information that maintenance crews can use to predict what might happen next and take corrective measures to address trending situations.

These systems have other benefits. The predictive analytics born from crunching enormous amounts of data is not subjective. Experts can use this information to make unbiased judgements on the cause of particular problems, helping to minimize unnecessary or improperly focused repairs.

The ultimate benefit of these solutions is their ability to move organizations from a preventive maintenance model to a predictive approach. Instead of following a predetermined schedule for maintenance and repairs, organizations that use these solutions can focus instead on the actual issues that are currently or might soon be affecting system performance. Applying this approach allows users to not only define the ideal time, but also the optimized scope of the maintenance conducted.

The Tipping Point

The capabilities of monitoring and analytics solutions continue to expand. But they have already reached a tipping point where they deliver significant benefits to organizations that adopt them.

Real-time remote condition monitoring gives end users a new smart tool for addressing equipment deterioration early on. When used in combination with data analytics, predictive maintenance systems help define optimal time and scope for maintenance and potentially identify the root cause of problems. They can help fuel continuous product improvement at OEMs, while also empowering end-users to make smarter, performance based-equipment decisions. Ultimately, they lead to lower operational costs and longer equipment service life.

The bottom line: If downtime of key equipment carries a significant cost, the Return on Investment in a stand-alone remote monitoring and analytics solution is typically more than worth the cost.

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