Machinery you can count on: implementing a profitable reliability strategy

In the past, measuring the reliability of industrial assets was limited to analysing historical asset performance with hopes that past behaviours would be replicated. Manufacturers have relied on various process control methods and applications for more than 100 years, the primary objective being to increase the plant’s throughput, i.e., its production, and safety.

To alleviate the strain being placed on plant assets, industrial maintenance tools and practices, intended to improve asset reliability, have progressed and evolved over the last two decades. Based on extensive laboratory testing and actual in-plant experience, there is already considerable information on reliability at the equipment asset level. For example, accurate reliability curves, coupled with condition and process measurement, enable accurate measurement of asset reliability risk.

The effect of all this is that companies are now paying much more attention to and driving advancements in plant maintenance. Over the past two decades, traditional reactive maintenance has evolved to include preventive, predictive and prescriptive maintenance strategies.

The results have been promising, but improving business performance requires maintenance and operations strategies that collaborate much more than they do today. If the ultimate objective is for both maintenance and operations to maximise operational profitability, approaching reliability, efficiency and profitability from a common strategic plane is essential. This collaborative approach is referred to as profitable reliability.

Advancements in data science and the proliferation of condition and process measurements in industrial operations are making the direct real-time measurement of asset reliability feasible. Such measurement will, in turn, make more sophisticated, real-time approaches to controlling asset reliability feasible too.

Developing a profitable reliability strategy might seem daunting, but some fairly simple steps can help move industrial operations in the right direction:

  • Identify the critical equipment that represent the largest opportunity for performance improvement. Very often this will be rotating equipment because their mechanical movement tends to wear the asset over time.
  • Determine what measurements are required to analyse the equipment’s performance.
  • Use the process and condition measurements to calculate the asset’s maintained state and its probability of failure.
  • Develop an asset control scheme that includes integrated reliability and process control strategies that maximize operational profitability. These might include reducing the output of the asset to extend its time to failure so you can finish a run or a contract.

Move the reliability measurement and control up to the next level asset set (for example, the process unit) and perform the same control strategy analysis. This analysis should be simpler to perform once the base equipment level assets are under control.

Continue this process all the way up the asset hierarchy until you have real-time control strategies in place for all your critical assets and asset sets. This would include process areas, plants and even enterprises.

Empowering today’s industrial workforce with real-time operational profitability data, along with process control and real-time reliability risk information, will turn them into operations and business performance managers. Operators will be able to adjust set points and see the impact they and their adjustments are having, not only in the process, but on the profitability and reliability of the assets too.

We can therefore see that a profitable reliability approach that combines real-time reliability risk control, real-time operational profitability control and higher-level reliability management will go a long way toward helping industrial manufacturers meet their short and long-term operations, and business objectives. The result will be greater levels of operational profitability, safety, and reliability.

Written by Brad Yager, director process automation and software at Schneider Electric.


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