Predict Failure versus Predictive Maintenance

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In a recent post by ARC Advisory Group, Peter Reynolds notes that 80% of assets fail randomly despite being supported by programs designed for asset maintenance and reliability. Only 3-5% of maintenance performed is predictive. The vast majority of maintenance is either break-fix or executed based on the OEM’s asset maintenance schedule – needed or not.

A broad set of factors drive asset performance, including variabilities in process conditions/flow outside the asset itself, which previously may not have been considered relevant to determining asset condition. With advanced analytics, the compute power is available to combine asset health, asset condition, and process variables to determine the asset’s true risk of failure.

More importantly, machine learning will provide a means to see beyond a conventionally-understood state leading to asset failure. These machine learning models require an understanding of the operating and failure mode states of these assets. As Reynolds points out, this probably means working with operating personnel, not maintenance personnel, to develop the models. This marks a change from condition-based maintenance and less sophisticated predictive models.

Using sophisticated machine learning models, asset managers can know that a given asset will continue through a rough spot, not fail as might have been predicted by condition monitoring or prognostic models, and will in fact go on to a longer operation. This suggests that the P-F curve in ARC’s post could look more like a sine wave than a gradual drop off. The key is to have confidence in the algorithm’s prediction that failure is actually not imminent. Only the right set of machine learning analytics can predict into the future without a loss of confidence.

Predictive and prescriptive analytics will indeed drive the next wave of improvements in asset performance. But only the right algorithms will provide the highest return on investment for those seeking lasting improvements in asset performance.

 

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Re-imagining the Future of Asset Maintenance

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Asset failure, or more accurately, avoiding asset failure, is big business, as it should be. For asset-intensive industries, asset failure can mean revenue loss, customer dissatisfaction, brand degradation, even regulatory fines. So improving the means by which asset failure is avoided is as important as the day-to-day production by the asset.

Many companies continue to take a break/fix approach to asset repair, or cyclical preventative maintenance, where pre-set characteristics of general asset types determine when maintenance is performed. Some are considering Condition-Based Maintenance (CBM), where some parameter of an asset is monitored and repair is performed when that parameter indicates a problem or imminent failure, based on a statistical models for that type of asset. But greater business benefits are achieved with Predictive and Prescriptive Maintenance, often powered by machine learning, which look at the state of each individual asset and predict the probability of failure into the future, and optimize maintenance and repair schedules based on that input along with other constraints.

Arc Advisory Group recently updated its Maintenance Maturity Model to note the availability and benefits of these more sophisticated analytic approaches. They noted that moving from preventive maintenance to predictive and prescriptive models can deliver 50 percent savings in labor and materials, which has a ripple effect from improvements in shipping times to customer satisfaction. They observe that new technologies in the industrial internet of things (IIoT) enable inexpensive, real-time asset monitoring. Measuring vibration, heat, lubricants, and other asset conditions in real-time are essential for the enterprise to adopt Predictive and Prescriptive Maintenance. Creating a ‘digital twin’ or software model of the asset gives analytics software a basis to compare ideal and observed measurements.

Doesn’t CBM provide many of the same benefits? Perhaps to a lesser extent, but there is no reason to settle for CBM. In CBM, analytics examines the current state of the asset to alarm for likely asset failure. However, not every condition that may appear to head toward failure actually will in that specific asset, and true asset maintenance optimization can occur only when an enterprise can reliably determine the difference. Avoiding unnecessary maintenance costs can extend asset life at a fraction of the cost.

Predictive maintenance powered by machine learning should allow you to ‘see over the hill,’ beyond the current condition, to determine the most probable outcome given the current condition of each asset. The combination of machine learning and IIoT could prove to be the missing link in smart and effective asset maintenance.

 

 

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