Regular equipment maintenance is important and necessary to ensure the proper performance and working condition of manufacturing equipment. If not done in time, you risk unexpected equipment breakdown, reduced overall lifetime, increased maintenance costs, wasted energy and further disruption. Although preventive maintenance can minimize these risks, it can also lead to over maintenance with unnecessary costs, equipment downtime and reduced output.
Predictive maintenance helps determine the condition of the equipment to estimate when maintenance will be necessary. It allows convenient scheduling of corrective maintenance when needed and can prevent unexpected equipment failures. By turning unplanned stops into shorter and fewer planned stops, you can increase equipment availability and lifetime, improve safety and environmental impact and optimize spare parts handling. It is also an Industry 4.0 initiative.
Rule-based predictive maintenance requires continuous data collection by sensors; hence, another name for this is condition monitoring. Predefined rules and predefined thresholds generate alerts. Machine learning-based predictive maintenance relies on large sets of historical failure data used with machine-learning algorithms to run different scenarios and estimate the probability of things going wrong, and when. This data- and model-driven approach provides insights for maintenance and repairs allowing organizations to avoid unscheduled disruptions in operations or production.
Laboratories with their large number of costly and sensitive testing instruments are one application area where predictive maintenance can make a difference. It can help in the production of pharmaceuticals, household or cosmetic products. It can also help process industries in the production of chemicals as well as discrete manufacturing industries like automotive, aerospace or industrial equipment. In all these industries, delays in time to market can result in significant losses in revenue or market share.
How does it help?
Machine learning-based predictive maintenance utilizes a large amount of historical data collected from a substantial number of instruments (about 100) that are equipped with sensors connected to an IoT (Internet of Things) platform. These sensors measure and deliver data related to voltage, temperature, pressure, rotation and vibration for a full year. The data might be streaming data that requires real-time processing, averaging or sampling on the fly. This “telemetry” data contains time stamps (e.g., 2018-03-26T20:36:02.316Z) and includes contextual data on machine age and type. Hereby the system excludes “error” and/or “on-breaking” signals sent out by the machines at particular times.