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temperature, pressure, and electrical signals. Unlike traditional maintenance methods that depend on fixed schedules, condition-based monitoring (CBM) initiates maintenance actions only when data signals signs of deterioration. Predictive maintenance advances CBM by utilizing historical and real-time data, advanced analytics, and machine learning algorithms to predict when a machine is likely to fail. This allows manufacturers to schedule repairs in advance, reducing unplanned downtime and optimizing maintenance resources for improved operational efficiency. Key Sensing Modalities for Equipment Health To effectively implement CBM and predictive maintenance, manufacturers rely on several sensing modalities: 1. Vibration sensing: Widely utilized for monitoring rotating equipment, changes in vibration patterns frequently indicate issues such as imbalance, misalignment, or bearing wear. 2. Temperature monitoring: Tracks heat buildup that may signal friction, electrical faults, or lubrication failures. C h a p t e r 1 | C o n d i t i o n M o n i t o r i n g a n d P r e d i c t i v e M a i n t e n a n c e 3. Magnetic field sensing: Identifies problems in electric motors and generators, such as broken rotor bars or winding faults, by observing anomalies in magnetic flux patterns. 4. Electrical measurements: Voltage and current sensing provide insights into motor health, load conditions, and power quality, with abnormalities often preceding mechanical and thermal faults. By integrating data from multiple sensing modalities, manufacturers gain a comprehensive understanding of equipment health, enhancing fault detection accuracy and enabling targeted maintenance strategies. As the manufacturing industry evolves, embracing predictive maintenance and automation technologies will be essential for companies aiming to optimize operations, reduce costs, and maintain a competitive edge. Manufacturers can unlock new levels of efficiency, reliability, and sustainability by harnessing the power of real-time data and advanced analytics, paving the way for a smarter, more resilient future. 5 Engineering Reliable Industrial Automation With Sensor Fusion