Predictive maintenance (PdM) is the proactive maintenance of equipment, in which data about the equipment's current state is used to predict when future maintenance will be necessary. This allows for the scheduling of maintenance before problems occur, which can improve equipment uptime and reduce maintenance costs.
PdM can be used for a variety of equipment, including production machinery, vehicles, and HVAC systems. Data about the equipment's state can be collected using sensors, and this data is then analyzed to predict when maintenance will be needed. The analysis can be done using a variety of methods, including statistical analysis, machine learning, and artificial intelligence.
PdM is often used in conjunction with other maintenance techniques, such as condition-based maintenance (CBM) and preventive maintenance (PM). CBM is a reactive maintenance technique in which equipment is only repaired or replaced when it is already showing signs of wear or failure. PM is a proactive maintenance technique in which equipment is regularly inspected and serviced, even if it is not showing any signs of wear or failure.
PdM can improve equipment uptime and reduce maintenance costs by allowing for the proactive scheduling of maintenance before problems occur. PdM can also improve safety by reducing the need for manual inspection of equipment, which can be dangerous if the equipment is in a hazardous environment.
What is PM and PdM?
The Internet of Things, or IoT, is a system of interconnected devices and sensors that can collect and share data. PM, or predictive maintenance, is a type of IoT application that uses data to predict when equipment will need maintenance or repair. PdM, or preventive maintenance, is a type of maintenance that is scheduled based on time or usage rather than on data.
What are the three types of predictive maintenance?
The three types of predictive maintenance are condition monitoring, failure mode analysis, and reliability analysis.
Condition monitoring is the practice of monitoring the condition of equipment in order to identify potential issues that could lead to a failure. This can be done through a variety of methods, such as monitoring vibration levels, inspecting for wear and tear, and using thermal imaging to identify hot spots.
Failure mode analysis is the process of identifying the different ways that a piece of equipment can fail. This information can then be used to develop strategies for preventing or mitigating the effects of those failures.
Reliability analysis is the process of analyzing the reliability of a piece of equipment. This can be done through a variety of methods, such as looking at historical data, conducting failure analysis, and performing statistical analysis.
What is predictive maintenance?
Predictive maintenance is a type of maintenance that uses data and analytics to predict when equipment is likely to fail, so that repairs can be scheduled in advance. This can help to avoid downtime and keep equipment running smoothly.
Predictive maintenance can be used on a variety of equipment, including HVAC systems, manufacturing machinery, and vehicles. In order to be effective, predictive maintenance requires data about the equipment, such as operating data, maintenance history, and repair data. This data can be collected manually or through the use of sensors and other data-gathering devices.
Once the data has been collected, it can be analyzed to look for patterns that indicate when equipment is likely to fail. This analysis can be done manually or through the use of predictive maintenance software. Predictive maintenance software can be used to create models that can be used to predict when equipment is likely to fail, based on the data that has been collected.
Predictive maintenance can be used to schedule repairs in advance, so that equipment can be repaired before it fails. This can help to avoid downtime and keep equipment running smoothly.