Production facility managers typically use corrective maintenance which is costly in terms of downtime, labor use, working capital, and unscheduled maintenance. The cost of machine downtime is high: according to the International Society of Automation, $640 billion is lost every year. Also, at a plant level a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime. While another approach is preventive maintenance, where components are replaced after a given time, regardless of their condition. This increases the operative costs because preventive maintenance may replace parts that still have significant working life. which is waste of time and money.

ICURO manufacturing AI practitioners guide global manufactures to transition from corrective maintenance to predictive maintenance. Having seen the cost associated with both approaches, advanced predictive maintenance capabilities can be delivered using machine learning and deep learning techniques. This approach minimizes the cost of unscheduled maintenance and maximizes the component’s lifespan, thus getting more value out of a part. With AI and machine learning, we have the ability to process massive amounts of sensor data faster than ever before. This gives companies an unprecedented chance to improve upon existing maintenance operations and even add something new: predictive maintenance.

Our manufacturing AI systems look for patterns in the data to identify failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows. To achieve above capability in the legacy systems, our machine learning algorithms are given data ingestion from the production floor sensors, programmable logic controllers and SCADA systems, IT data including the contextual data like ERP, quality control, usage history and service history data of machines to reflect the machine deterioration process.

Our predictive maintenance tools upgrade your existing maintenance systems by applying AI to ensure that your people have the right knowledge and tools to keep your mission-critical assets running at peak performance. We deploy a remaining useful life (RUL) predictor using improved prediction model combining inverse exponential smoothing and Markov chain which is a random time series analysis. The deductive learning for time window classifier are applied to deliver advanced classification models.