PdM systems typically collect data from sensors on equipment, such as vibration, temperature, and current. This data is then analyzed to identify patterns that indicate potential problems. Machine learning algorithms are used to learn these patterns and predict when a failure is likely to occur.
Once a failure is predicted, PdM systems can send alerts to maintenance personnel so that they can take corrective action. This could involve preventive maintenance, such as replacing worn parts, or simply monitoring the equipment more closely.