The difference and characteristics of preventive maintenance and predictive maintenance systems

▲ Condition monitoring area under PF curve

Preventive maintenance and predictive maintenance are two different maintenance strategies, which differ in objectives, methods and characteristics. These two strategies can complement each other to improve equipment reliability and reduce costs in maintenance management.

▌ Preventive maintenance

Preventive maintenance is a regular maintenance strategy based on time or usage statistics. Keep equipment in good working condition and reduce breakdowns and downtime through preventive measures such as regular inspection, cleaning, lubrication and replacement of parts.

Main feature

■ Periodic: Preventive maintenance is carried out according to the plan and schedule, regardless of the actual condition of the equipment and signs of failure;

■ Maintenance intervention: through inspection, maintenance and replacement of parts, etc., to prevent equipment failure and maintain its normal operation;

Preventive: The purpose of preventive maintenance measures is to prevent failure and damage, and to take measures in advance to maintain the reliability of the equipment;

Data analysis

Maintenance record analysis: By analyzing device maintenance records, including maintenance frequency, maintenance history, and maintenance results, you can discover common fault modes and maintenance requirements.

Failure Mode and Impact Analysis (FMEA) : Used to identify and evaluate potential failure modes, failure causes, and their impact on equipment performance. By analyzing device failure history and usage environment, you can identify key components and potential fault sources, and formulate maintenance strategies.

Statistical analysis: The use of statistical methods, such as failure rate analysis, reliability analysis, and trend analysis, to assess the reliability of equipment and predict the probability of equipment failure.

Parallel analogy between human body and machine maintenance

Technical body analogy for maintenance strategy

Preventive maintenance regular parts replacement transplant surgery

Predictive maintenance monitors vibration

Wear particles detect heart disease with electrocardiogram or ultrasound

▌ Predictive maintenance

A maintenance strategy based on real-time monitoring and data analysis. By collecting and analyzing equipment operating data to detect potential signs of failure and predict equipment life, maintenance measures can be taken accordingly. The goal of predictive maintenance is to minimize sudden failures and downtime and improve equipment availability and productivity.

Main feature

Data-based: Predictive maintenance relies on real-time monitoring and data collection to detect early signs of failure through the analysis and interpretation of equipment data;

■ Failure prediction: By analyzing data and models, predict the life of equipment and the possibility of failure, so as to take timely maintenance measures to prevent failure;

■ Personalized maintenance: Predictive maintenance according to the actual condition and data of the equipment, develop personalized maintenance plans and strategies to reduce unnecessary maintenance work and costs.

Data analysis

Data acquisition and sensor technology: Predictive maintenance relies on real-time data from equipment, such as vibration, temperature, pressure, etc., which can be collected through sensors. The data acquisition system can continuously monitor the operating status of the equipment and record relevant data for subsequent analysis.

Machine learning and data modeling: Through machine learning and data modeling techniques, patterns and correlations can be discovered from large amounts of device data. These models can be used to predict equipment failures, remaining life, and forecast maintenance needs to provide accurate maintenance planning and early warning systems.

Fault diagnosis and intelligent analysis: Based on real-time data and equipment models, fault diagnosis and intelligent analysis technology can monitor equipment status in real time, identify abnormal behavior, and provide accurate fault diagnosis and recommended maintenance measures. This helps to anticipate and avoid equipment failures in advance, reducing downtime.

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