Predictive maintenance (PdM) and condition monitoring systems are used to predict equipment or component maintenance, and to monitor corrosion, oil condition, arcing, bearing wear, overheating and other parameters that can lead to the breakdown of capital equipment. They incorporate technologies such as vibration measurement and analysis, infrared (IR) thermography, oil analysis and tribology, and motor current signature analysis (MCSA). Vibration analysis systems apply the principle that all moving bodies produce sound waves. By contrast, predictive maintenance and condition monitoring systems that use infrared thermography measure the emission of heat. Oil analysis and tribology systems are used to monitor the friction, lubrication and wear of interacting surfaces in relative motion. Motor current signature analysis (MCSA) systems provide information about electrical faults caused by excessive motor starts or overloading. Because rotor bar damage is difficult to detect with a vibration signature, MCSA systems are often used to prevent motor failure with mission-critical machinery.
Many predictive maintenance and condition monitoring systems use acoustic vibration monitoring (AVM) to identify vibrations which correspond to specific defects. There are several different AVM techniques. Low-frequency vibration measurement systems are used to indicate that a component is in advanced state of wear because of problems such as imbalance or misalignment. Service personnel can then repair machinery or equipment before component failure occurs. High-frequency vibration measurements also help to prevent downtime, but typically trigger alarms before a mechanical component enters a failure state. In some industries, ultrasonic detectors are used to translate inaudible frequencies into a 200 - 5000 Hz range that humans can detect. Devices with digital measurement capabilities can store measurements and upload data to a condition monitoring software system (CMSS). Often, these predictive maintenance and condition monitoring systems are handheld.
Predictive maintenance (PdM) and condition monitoring systems are used to predict equipment or component maintenance, and to monitor corrosion, oil condition, arcing, bearing wear, overheating and other parameters that can lead to the breakdown of capital equipment. They incorporate technologies such as vibration measurement and analysis, infrared (IR) thermography, oil analysis and tribology, and motor current signature analysis (MCSA). Vibration analysis systems apply the principle that all moving bodies produce sound waves. By contrast, predictive maintenance and condition monitoring systems that use infrared thermography measure the emission of heat. Oil analysis and tribology systems are used to monitor the friction, lubrication and wear of interacting surfaces in relative motion. Motor current signature analysis (MCSA) systems provide information about electrical faults caused by excessive motor starts or overloading. Because rotor bar damage is difficult to detect with a vibration signature, MCSA systems are often used to prevent motor failure with mission-critical machinery.
Many predictive maintenance and condition monitoring systems use acoustic vibration monitoring (AVM) to identify vibrations which correspond to specific defects. There are several different AVM techniques. Low-frequency vibration measurement systems are used to indicate that a component is in advanced state of wear because of problems such as imbalance or misalignment. Service personnel can then repair machinery or equipment before component failure occurs. High-frequency vibration measurements also help to prevent downtime, but typically trigger alarms before a mechanical component enters a failure state. In some industries, ultrasonic detectors are used to translate inaudible frequencies into a 200 - 5000 Hz range that humans can detect. Devices with digital measurement capabilities can store measurements and upload data to a condition monitoring software system (CMSS). Often, these predictive maintenance and condition monitoring systems are handheld.
Predictive maintenance and condition monitoring systems are used in paper mills, chemical processing plants, oil refineries, and the pharmaceutical industry. They are also used in marine and manufacturing applications. Although predictive maintenance and condition monitoring systems vary in terms of features and cost, they consistently improve equipment reliability through the prediction and avoidance of costly failures. Predictive maintenance and condition monitoring systems also minimize downtime by enabling facilities to plan and schedule repairs. In turn, component life is maximized by avoiding conditions such as lubrication contamination and misalignment.
