Predictive maintenance is a type of maintenance that tracks an asset's health, performance, status, safety, and various related metrics. Typically, the goal of predictive maintenance is to reduce expensive mishaps, prevent downtime, and offer manufacturers the opportunity to plan maintenance around their own production schedule.
Predictive maintenance continuously analyzes the condition of equipment during operations in order to reduce the possibility of machine failure or unexpected issues. This is done through a combination of real-time data collected through the industrial internet of things (IIoT).
Using predictive maintenance, organizations are allowed to monitor various indicators, such as slow bearing speed, temperature, etc. By utilizing condition-based monitoring and IIoT technology, these tools can detect abnormalities and send real-time alerts to indicate potential failure in the future or alleviate any mishaps in operation.
Other specific types of predictive maintenance include vibration, infrared, and sonic acoustical analysis. Let's review these:
Vibration analysis- This type of predictive maintenance can be used inside manufacturing plants with rotating machinery. The particular type of analysis detects imbalances, loose parts, misalignment, and other complications.
Infrared analysis- This type of predictive maintenance uses temperature as an indicator to identify issues such as airflow, cooling, and motor stress.
Sonic acoustical analysis- This type of predictive maintenance utilizes sounds that can be converted to an auditory or visual signal to be heard/seen by a technician. This indicates conditions such as worn bearings or under-lubricated bearings.
In addition to predictive maintenance, there is also preventive maintenance. Although these terms are often used interchangeably, they present many differences. While preventive maintenance occurs at regular intervals based on the machine's lifecycle, predictive maintenance only occurs when it is required, based on the machine's insights. Preventive maintenance occurs regardless of usage to ensure that future issues don't occur. Predictive maintenance utilize machine insights provided by the IoT sensors, so as the sensors wear overtime, manufacturers are allowed to proactively initiate maintenance.
Additionally, there is condition-based maintenance. Although both of these forms of proactive maintenance prevent machine failure, there are still prominent differences between the two. Condition-based maintenance, or CBM, utilizes sensors in order to collect measurements from a piece of equipment - such as pressure, temperature, vibration, or other similar measurements. Once the condition status hits a certain threshold or demands service, service is delivered. Predictive maintenance uses the stream of constant IIoT sensor data to predict machine degradation - based off of asset history and related data. Predictive maintenance provides technicians with the ability to catch potential issues even earlier, allowing service to be scheduled more efficiently. However, condition-based maintenance can present the risk of multiple machines needing service at the same time.
5 Technological Advantages of Predictive Maintenance:
- Decreased downtime- By detecting issues in advance, you're now able to resolve problems beforehand.
- Increased productivity across organizations- The need for disruption in worker productivity is obsolete, due to the fact there is no unexpected breakdowns. Predictive maintenance can be planned conveniently, around workers' schedules.
- Reduced field service costs- By anticipating maintenance, service departments can now save costs and increase ROI.
- Enhanced product design and quality- Utilizing the power of IIoT data, workers' can utilize crucial information such as durability, reliability, efficiency, etc.
- Increased worker and workplace safety- Unexpected breakdowns, malfunctions, or equipment issues can lead to hazardous conditions. By avoiding malfunction, workers are now able to avoid machines when they are compromised or carry out services before machines become dangerous.
To implement predictive maintenance efficiently and reap all of the important benefits, you should make sure you complete some initial tasks - including design your program, install IIoT devices, perform system integration, schedule maintenance, etc.
Overall, predictive maintenance is proving to offer countless benefits enterprise-wide - from reducing downtime to saving cost, streamlining processes, preventing potential hazards, and more.