The increase in the use of Machine Learning (ML) and in the levels of automation in manufacturing has naturally led to improving asset productivity through data-driven insights that were not possible before. Now we see predictive maintenance taking the place of the “run to failure” approach where asset maintenance is scheduled once a failure in the system is observed. Predictive maintenance is a set of processes that reduce maintenance costs and prevent extra time being wasted on repairing assets – particularly in dependent machinery systems.
In order to detect changes in the system that point to potential “health problems” in machinery, pattern-recognising machine learning models are deployed. Manufacturing companies are using predictive maintenance software to reduce unplanned downtime and maximise profitability. Machine learning predictive maintenance is unique because it not only predicts impending failure but produces outcome-focused recommendations for operations and maintenance from analytics.
How Does This Work?
The effectiveness of ML predictive maintenance models relies on the health indicators of the machinery. Historical data from these health indicators (features) is analysed by ML models. The ML models then compute another indicator which describes the machinery’s health and an estimated remaining lifetime.
The below are the base components for predictive maintenance to be carried out:
Data Sensors – sensors that collect data are installed on the machinery
Data Flow – a communication system allows machinery data to flow to a data storage centre
Data Storage – a central hub stores, processes, and analyses the machinery data either on the premises or on the cloud
Predictive Analytics – algorithms are applied to the processed data, patterns are recognised, and insights in the form of dashboards and alerts are created
Cause Analysis – corrective action is determined based on machinery health insights
When is Predictive Maintenance Being Used?
Manufacturing companies (and many other companies across different industries) use ML predictive maintenance in a number of ways. Manufacturers of machines and machinery parts use predictive maintenance to analyse and monitor the condition of motors and their levels of wear, power consumption, and productivity. Predictive maintenance is also being used to minimise production errors and reduce waste. ML models can predict when the number of faulty products is likely to exceed a set maximum percentage and give causes for the expected problems with the products. Manufacturers are also using ML predictive maintenance models to predict issues across the factory floor: by linking all monitored sections with installed machine sensors, security cameras, worker equipment, and workstations.
Manufacturing organisations who have already implemented ML into their daily functioning are seeing great benefits of predictive maintenance – an increase in information delivery speed, an increase in usefulness of information, and decreases in costs. The ML predictive analysis has allowed organisations to make smart decisions based on their own data. This results in a complete solution across operations, engineering, and maintenance. Machine learning predictive maintenance plans give managers unmatched insights into operations that can lead to optimised safety, efficiency, and decision-making. Taking a predictive maintenance approach to asset performance and reliability means manual root cause analyses are no longer needed; instead ML models will consider an entire history of asset performance and failures to identify signs of impending problems in advance.
Contact Intellify, the AWS Partner of the Year 2019 in Data, Analytics, & Machine Learning, to learn more about how your organisation can benefit from machine learning & artificial intelligence. We provide end-to-end services, helping companies reach their full potential by leveraging data proactively.