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Analytics-Based Predictive Maintenance

Digital transformation continues to change the way we conduct business.

Periodically, the introduction of a new digital technology accelerates the rate of change, and that is exactly what is happening in one area of our business: equipment maintenance. Traditional maintenance programs that consist of various strategies are rapidly being replaced by analytics-based predictive maintenance (PdM) programs where actual equipment conditions are monitored 24/7, allowing the data used to make predictions about future equipment failures at the earliest moment. Predictions are then used to make better informed decisions regarding equipment maintenance. These informed decisions increase system reliability and help corporations manage and decrease costs by avoiding predictable equipment failures and downtimes. In addition, analytics-based PdM programs help extend equipment lifespan, optimize assets and resources, and reduce potential risks.

The digital technology behind the rapid change to analytics-based PdM programs is the combination of smart technologies with data analytics. Smart technologies are those where physical objects, embedded with electronics, software and connectivity, communicate with each other and people. This communication can be wireless, via the internet or a combination of both. The network through which our “smart” objects communicate is known as the Industrial Internet of Things (IIoT). Smart technologies operate to some extent interactively and autonomously, and may involve ubiquitous computing, i.e., anytime/everywhere computing that may employ artificial intelligence. Data analytics involve the computational analysis of bodies of data to identify patterns, trends, and associations; then interpret, communicate, visualize what they represent; and, finally, apply those findings.

The powerful combination of smart technologies with data analytics tools enables businesses to collect real-time data on their equipment that is then transmitted to computers for immediate or on-demand analyses. Then, depending on the system, computer- or human-generated actions may be initiated in response to those analyses. This process can be partially or completely automated.

Some in our industry are still skeptical that analytics-based PdM programs are more valuable than traditional maintenance programs. They question the assertions that 1. Analytics-based PdM programs deliver quicker and higher-cost savings than a traditional maintenance program, and 2. Cost savings are substantial and realized soon enough to constitute a relatively rapid return on the investment made to implement the analytics based PdM program.

For some skeptics, just a few lucrative “saves” are sufficient to convince them of the value of an analytics-based PdM program. Others are convinced only after being provided with the results of cost benefit analyses. Some cost-benefit analyses indicate that on average, analytics-based predictive maintenance can reduce failures by 70 percent and lower maintenance costs by 25 percent. A major benefit that does not show in these statistics is an improved safety environment. Safety is improved by reducing risks of climbing ladders, working in tight spaces and around hot surfaces to obtain data with “walk-around data collection.” Reduced man-hours spent repairing equipment, especially in emergencies, equates to less chance of work-related injuries. Finally, the possibility of catastrophic industrial accidents is greatly reduced with equipment that is more reliable.

There are those in our industry who still conduct reactive maintenance on their equipment, and others are using analytics-based PdM programs that are not optimized. The future success of these organizations may be limited or in jeopardy because they have not yet harnessed the full power of smart technologies combined with data analytics. However, it is not too late for these firms to catch this wave of digital transformation. Benefit analyses are available from a number of resources. For a company still conducting reactive maintenance, the results can be used to justify and design an analytics-based program. For a company with a pre-existing analytics-based PdM program, the results can be used to justify continued use and/or expanding an analytics-based program.

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