Prescriptive maintenance technology is transforming asset performance management with the premise why simply predict production issues when you can prescribe fixes for them and act on the prescriptions. Predictive maintenance models have advanced to the stage where they are able to take into account the articulated production and resource usage goals of an enterprise or organization. Prescriptive maintenance is the asset maintenance strategy that uses machine learning to adjust operating conditions for desired outcomes, as well as intelligently schedule and plan asset maintenance.
Using machine learning and artificial intelligence to anticipate asset maintenance requirements, predictive maintenance can help avoid impromptu corrective maintenance by allowing for plant maintenance to be effectively scheduled prior to equipment failure.
Prescriptive maintenance, on the other hand, not only looks for failure signatures, but also provides information about how to delay or entirely eliminate equipment failure. These algorithms can comb historical data for examples of a wide variety of operating conditions, and also extract patterns and extrapolate data to provide hypothetical operating environments. The cascade of effects and consequences from small adjustments to an industrial process can be simulated by the prescriptive maintenance model, allowing for expensive and risky experimentation to be performed in a computer simulation.
In order for prescriptive maintenance to be effective, a machine learning model needs to be trained on historical sensor and service data. The more high-quality information available, the more accurate the artificial intelligence model will be–identifying more signs of maintenance needs and failure signatures, while producing fewer false positives. Data may need to be cleaned before being fed to the machine learning algorithm. For example, sensor values may need to be adjusted to reflect changes in calibration or standardize how different errors are coded by human operators.
Higher-level information about an organization can be provided to the machine learning algorithm when training a prescriptive maintenance algorithm. This allows the software to take strategic considerations, such as cost of repairs and manufacturing downtime, into account.
The training of the machine learning model occurs on specialized hardware, either stored locally or in the cloud. The model is code that can be deployed on site or in the cloud, so some way of accessing and running the model is required. This can be directly integrated with many asset management software suites, simplifying the process of integrating the recommendations of the prescriptive maintenance model.
Finally, prescriptive maintenance requires a company to be willing and able to implement the recommendations of the machine learning model. The hypothetical outcomes produced by a prescriptive maintenance program offer choices that previously had been left to chance, or had followed a try-it-and-see approach. This can lead to interdepartmental conflicts, especially considering prescriptive maintenance takes into account financial and operational information when making recommendations.
Many fields and branches of industry have experienced the accuracy of predictive maintenance. The recommendations of prescriptive maintenance allow the power of machine learning to be applied more holistically to an enterprise or organization’s physical operations.
Where predictive maintenance provides information about a binary decision, such as the choice to perform or defer asset maintenance, prescriptive maintenance offers a suite of options and outcomes from which to select. For example, a full stop in production may be avoided by running a compressor at a lower pressure. Or, a plant may keep a machine’s speed below a certain threshold, thereby pushing back the planned downtime in order to coincide with the delivery of a new piece of equipment.
Prescriptive maintenance can identify capital expenditure requirements months before they would become apparent to human operators. For example, prescriptive maintenance tools can serve as a digital testing environment in which the results of adding equipment can be simulated before making an acquisition. This allows an enterprise or organization to time purchases and acquisitions more economically.
What benefits does prescriptive maintenance provide?
Prescriptive maintenance provides the opportunity for increased efficiency by minimizing downtime. Machine learning can streamline asset maintenance and reduce downtime by recommending maintenance only when required. The ability of prescriptive maintenance tools to anticipate and recommend when to perform plant maintenance takes much of the guesswork out of reliability management. Prescriptive maintenance also allows reliability management to become more flexible in the recommendations about plant maintenance.
What are some prescriptive maintenance tools?
There are a variety of prescriptive maintenance tools that seamlessly integrate with asset performance management software. IBM’s Maximo APM, Oracle Analytics Cloud, GE APM Reliability, and AspenTech’s Aspen Mtell provide prescriptive analytics options and abilities.