Condition-based Monitoring (CbM) and Predictive Maintenance (PdM) are crucial for deploying industrial 5.0 maintenance strategies. They allow companies to monitor the health of machinery and equipment in real-time to enable timely interventions.
Modern Industrial Issues
Traditional maintenance approaches are typically reactive or scheduled, where technicians may be called in or held on standby to monitor or address machine health issues. Over-maintenance, or the excess deployment of personnel or maintenance supplies can also be costly. Condition-based monitoring allows a timely and efficient strategy of keeping downtime and maintenance costs to a minimum.
Reactive Maintenance
Complex industrial environments can be difficult to diagnose when machine degradation or failure occurs. Technicians may spend an excessive amount of time performing repairs.
Unplanned Downtime
Unplanned industrial equipment downtime can cost some companies billions in annual costs. In some cases, production is halted altogether.
Over-Maintenance
Current machine health monitoring may employ routine checkups that may not be necessary, often times slowing down productivity. Spare parts and other support material/technicians may also be inefficiently allocated.
edgeRX Condition-Based Monitoring
Condition-based monitoring relies on data collected from sensors and diagnostic tools to assess equipment condition.
- Data is collected via sensors that live on or near machines (the edge) to detect vibrations and temperature.
- TDK SensEI’s edgeRX machine learning AI analyzes the data to identify specific machine states, including operational, idle, and faulty.
- Once data is analyzed, AI models are installed onto the sensors to begin real-time monitoring. Users are flagged when anomalies are detected (when machines degrade/break down). This informs technicians which machines and where in an environment fault states occur, reducing unplanned downtime.
- Most condition-based monitoring (CbM) systems are limited to detecting well-known ISO standards. edgeRX’s AI solution goes beyond these limitations by identifying additional fault states that traditional ISO standards might overlook.
edgeRX Predictive Maintenance
- Predictive maintenance is the next step in advanced machine health monitoring. With enough training, AI can now predict when in addition to where asset failure occurs.
- It improves upon CbM by using data inference to predict when machines will break down, improving repairs and minimizing downtime.
- Users can view the likelihood of machine fail-states and what machine parts will have issues (e.g. looseness, cavitation, etc). Each time predictions are made, the AI algorithm adapts and updates, increasing its accuracy.
- Maintenance can be accurately scheduled as users will have a deep understanding of their machine’s health at all times.
Contact TDK SensEI to see if edgeRX’s advanced machine health monitoring solutions can help your asset maintenance strategy.