Predictive maintenance

Inn-Force ML transforms machine time-series data into Uptime. Our approach to predictive maintenance, where artificial intelligence and machine learning are combined and leveraged to analyse data for early detection of potential issues, ensuring operational continuity and efficiency. Our framework signifies a shift from reactive to proactive maintenance, optimising the lifecycle of machinery and reducing downtime.

Inn-Force ML is tailored for predictive maintenance, harnessing the power of advanced analytics to anticipate equipment failures before they occur. This innovative approach combines real-time data monitoring with machine learning algorithms to calculate the estimated remaining useful life of machinery, thereby optimizing maintenance schedules and reducing downtime.

Performance Monitoring

Inn-Force ML leverages data-driven insights and algorithms to monitor the health of machinery, thereby optimising maintenance schedules and reducing downtime. 

Inn-Force ML models analyse real-time data from IoT sensors to detect anomalies and forecast potential issues, allowing for timely interventions. Studies have shown that predictive maintenance can significantly reduce breakdowns, increase productivity, and lower maintenance costs. By integrating machine learning into maintenance practices, businesses can enhance operational efficiency and extend the lifespan of their equipment.

Implementing Inn-Force ML for predictive maintenance transforms traditional maintenance strategies into proactive measures that can predict equipment failure before it occurs. 

OEE

Integrating Overall Equipment Effectiveness (OEE) with time series data and machine learning can significantly enhance predictive maintenance strategies.

Inn-Force ML Time series analysis, which involves tracking changes over time, can detect patterns and forecast future performance of equipment.

Machine learning models, particularly those using supervised learning, can be trained on historical OEE data to predict future downtimes and maintenance needs.

For instance, techniques like takt time-based decision trees can be applied to create target-oriented OEE prediction models, which can outperform human predictions and traditional statistical methods.

IIoT Integration

Inn-Force ML supports industry standard protocols like MQTT, OPC UA, AMQP, SNMP, ModBus and REST API that are commonly used by the industry for their reliability and security in industrial settings. These protocols facilitate the efficient transfer of data across IIoT systems, enabling real-time decision-making and predictive analytics, which are essentially are the default protocols for modern industrial operations. 

By integrating Inn-Force ML with ERP and MES systems, industries can significantly enhance operational efficiency and reliability, ensuring a proactive stance on maintenance management while reducing costs significantly.