From Preventive Maintenance to Predictive Maintenance
Prevention is better than cure is an old saying. But the industrial world adopted formal preventive methods in manufacturing processes only in the late 50s and 60s. Among all industries, the airline industry was the first to deploy preventive maintenance in a big way since the cost of repair and the resultant downtime of commercial aircrafts led to significant losses. Other industries followed suit and preventive maintenance has become a standard in any manufacturing setup in today’s world.
Traditional preventive maintenance program is either time or run based and this approach has inherent limitations. Preventive maintenance is not future oriented and is more a reactive program.
With the introduction and adoption of big data, machine learning and artificial intelligence to solve business problems, industrial organizations and factories are beginning to make their preventive maintenance efforts predictive.
Machine learning algorithms can predict future failures and identify anomalies in equipment & machinery easily and effectively. The forecasted data of equipment and machines can then be used in myriad ways to drive an organization’s predictive maintenance program and to improve its processes.
An IT driven machine learning based predictive maintenance program can accurately forecast the likely period of failures of equipment and machines in future and this information can even be made available at a granular level. As an example, the likely time periods of the failure of a specific part in a machine can be predicted and the level of accuracy in prediction can be enhanced over time.
Not every organization has robust historical data to reap the benefits of machine learning and AI. For such organizations that have challenges with historical data of equipment performance, an IoT based system can be set up to capture and create a pipeline of historical data, which can then be used to identify future failures.
In a recent deployment of SeaportAI’s industrial analytics product in a large manufacturing organization, the down time reduced by 61% and the operating cost reduced by 23% in the first year itself. More importantly this deployment is contributing to a culture shift in the customer organization. The plant manager no longer asks for the preventive maintenance schedule during operations review. Rather the discussion is around the mitigation plan for the equipment that are likely to experience repair or failures in the near future.
As the industrial world embraces Industry 4.0, machine learning and AI based predictive maintenance program can make a big difference to how manufacturing operations are managed in future!
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