Power of Prescriptive Maintenance over Predictive Maintenance
Published on : Saturday 08-10-2022
Prescriptive maintenance is an improved maintenance strategy that uses the strength of machine learning, says Jasbir Singh.
Prescriptive Maintenance is a new evolving concept, which can integrate Condition Monitoring with Artificial Intelligence to manipulate available data to predict the real-time requirement for maintenance of equipment in loop/line or for device itself. Prescriptive Maintenance is an advanced version of Predictive Maintenance for the next level computation of condition monitoring solution of equipment/factory/plant/assets used in the entire business for manufacturing. It is capturing real-time data available from multiple sources of the equipment either by using the Internet of Things (IoT)/inline powered devices, to analyse data by using an analytical tool and understanding the current condition of the equipment.
Prescriptive Maintenance output is derived by advanced analytics to make close predictions for the maintenance requirements and recommendations with a choice of suitable options. The system does analysis to determine some possible maintenance options and potential outcomes of each, in order to quick and safe maintenance, and mitigate any risk while putting into operation. It can predict the optimum down time for the plant intervention/stoppage for maintenance.
Moving analysis model on condition-based maintenance
Considering the given recommendation for the maintenance action, the analysis still continues till it is done. During this time while the execution for maintenance is being planned/done, the system will constantly adjust the improved potential outcomes if required and provide revised recommendations if something significant needs to be done. Further after the maintenance activity has been completed it makes a fresh beginning. The analytical process is programmed for current condition monitoring of the said equipment and determines if the past maintenance carried out was effective and what the equipment health is likely to behave for the service it is meant for.
For Prescriptive Maintenance to be carried out effectively, asset management and right maintenance procedure are well integrated as part of execution. Data accuracy for Prescriptive Maintenance planning is imperative. Techniques of using big data, advanced analytics, continuous machine learning and Artificial Intelligence are crucial to adjust the final outcome.
Gathering of reliable data for perspective maintenance
The availability of right information and reliable data progressively strengthen the organisation by its ability to redefine the maintenance strategies. By this approach in plants, it significantly improves the effectiveness of the maintenance and reduces the overall cost of business operation. Predictive Maintenance by this approach in plant/equipment finds noteworthy savings and improvements in overall business output. The objective of the prescriptive maintenance approach is to focus more on plant critical assets, where during operation the unit is likely to see significant improvements in performance and equipment reliability.
With less availability of skilled automation level technicians in plant for operation and maintenance, prescriptive maintenance becomes more a necessity and important for the organisations to adopt at the early stage. The industry has already experienced the accuracy and returns of value from predictive maintenance. Prescriptive maintenance uses the power of machine learning which is applied holistically to enterprise level operations to gain the benefits.
New dimensions of condition monitoring
Prescriptive maintenance technology is in fact transforming the asset performance with the outcome of result post maintenance/availability, where a business can largely predict the production failure issues to fix them as prescribed by the system or act on the basis of prescriptions.
Predictive maintenance models have given good results, where plant operation staff are more articulated on production and resource, used to meet the goals of enterprise or organisation. Prescriptive maintenance, on the other hand, is an improved maintenance strategy that uses the strength of machine learning for prescription and to adjust operating conditions for the desired results. This system intelligently schedules and plans the maintenance of assets.
Predictive Maintenance Vs Prescriptive Maintenance
Using artificial intelligence and machine learning anticipates/forecasts the asset maintenance requirements, where predictive maintenance helps to avoid immediate corrective maintenance but effectively schedules the plant maintenance and allows it to be planned prior to equipment failure. The system progressively improves the confidence of the scheduled planned maintenance of equipment.
Prescriptive maintenance not only captures failure signatures, but it also provides information, how to minimise the correction/repairing work or even completely eliminate the possibility of equipment failure. The algorithms can dig out the historical data of similar patterns compared with present deterioration rate and a wide variation in operating conditions. It can extract similar patterns to extrapolate data and provide hypothetical operating environments for extended time of operation without failure. The small adjustments to the process parameters are simulated by the prescriptive maintenance model to check the failure trigger time, and if this can extend the operation allowing the machine to run further and avoid expensive prompt maintenance.
The level of prescriptive maintenance can be further enhanced and made more effective by using machine learning to train the model by available service data. With more and more quality information available by running the process, the precise and accurate artificial intelligence model can be developed to better signal/forecast maintenance needs and capture failure signatures. This advanced model provides fewer false initiations of maintenance requirements. The existing data need to be removed before being fed to capture the newer data for the machine learning algorithm.
Train the model for advance solutions
Advance-level information about the process needs to be provided for the superior machine learning algorithm when training a model of prescriptive maintenance algorithms. This allows the system to take precise strategic decisions, such as possible cost of repairs and total downtime, during maintenance.
To train the machine learning model it requires specialised hardware to develop for which the data is either stored on the cloud or on a local server. Further these developed algorithms used as models for perspective maintenance can easily be deployed either on premises (edge computing) or on the cloud computation, where these algorithms can be accessed to run the model again and again. These models can be directly integrated with other asset management software of business, for the integrated recommendations of the prescriptive maintenance solutions of the unit.
Prescriptive maintenance is successful if the business leaders are willing to carry out and accept the possible recommendations of the machine learning model. The speculative outcomes produced by prescriptive maintenance models offer choices, which had earlier been followed by a try-it-and-see-it approach. This sometimes leads to interdepartmental conflicts, while making the consideration of prescriptive maintenance outcome to take into account for financial and operational details when making the possible choice of recommendations.
Power of prescriptive maintenance over predictive maintenance
Predictive maintenance provides information about the single decision with no options or choice to consider, such as perform the maintenance or take conscious decision for the maintenance, whereas prescriptive maintenance offers a number of options and its possible outcomes from which the engineer can select the most suited suggestion. As such, a complete shutdown of the production line can be avoided by running the machine at a lower speed, if upstream and downstream processes allow it with the sacrifice of productivity. Or the plant may be run at lower speed, in order to delay the planned downtime to coincide with the delivery of high inventory finished products which is delayed in transit due to reasons beyond control.
Prescriptive maintenance models can also identify the capital budget requirements beforehand and it would become clear to the engineers/operators. Prescriptive maintenance systems can be used as a model testing tool to understand the results of adding new equipment in process and simulate the overall impact before making an acquisition plan. This allows time for an enterprise or organisation to make economical purchases.
Jasbir Singh is an Automation Expert having long experience in Factory Automation, Line Automation, Implementation Strategist, Business Coach, Regular writer on automation, Artificial Intelligence, Robots/Cobots, Digital Technology, Network Communication, Industrial Internet of Things (IIoT), Wireless Communication, Block Chain and use of advance digital technologies. He has established a long association with Business Houses/large production houses to improve factory automation in their production lines as well as productivity improvement in factories in India and overseas; and in advising and designing the units to transform into digital platforms by use of Artificial Intelligence. Email: [email protected]
