As data has become easier to collect, store and analyse, many maintenance professionals are turning to predictive led maintenance strategies.
Predictive Maintenance involves collecting data about the condition of an asset, and using the data collected to predict when the motor will fail. Consequently you can fix or replace a motor before a motor failure causes unplanned downtime.
The question is: why are maintenance professionals turning towards Predictive Maintenance as opposed to other maintenance strategies.
Predictive Maintenance vs Corrective Maintenance
Corrective Maintenance is perhaps the most basic maintenance strategy: A fault occurs and the service engineer must then correct the fault. Very little data is collected or used. This strategy is often best suited to environments where there are only one or two motors running, and the cost of unplanned downtime is very low.
If you compare this strategy to more modern maintenance strategies, Corrective Maintenance presents a number of disadvantages. For example, in the event of a malfunction or if a part breaks, production will stop. If it's going to take a long time to source a new part, or the maintenance staff are not available, the downtime could last for an extended period of time. For many industrial organizations, extended periods of downtime can severely damage productivity and profit.
Predictive Maintenance is based on the assumption that unplanned downtime is not an option. Various motor metrics are monitored, and if one of those metrics starts to indicate a fault, then the asset can be fixed or replaced in advance. Therefore, no sudden motor failures and no unplanned downtime.
Predictive Maintenance vs Preventive Maintenance
For some companies the cost of unplanned downtime is so high that they employ a Preventive Maintenance strategy. With this maintenance strategy, inspections are carried out early and parts are replaced, even before they show signs of a fault. This increases the reliability and availability of the assets, and can minimize the probability of unplanned downtime.
However, Preventive Maintenance often leads to unnecessary costs. Maintenance is scheduled early (often too early) to prevent assets from failing while in use. This in turn means assets are often replaced prematurely, which can substantially erode the return on investment of the machine, and increase TCO (Total Cost of Ownership).
Predictive Maintenance + Condition Monitoring
As mentioned, a Predictive Maintenance strategy requires the maintenance team to predict when a fault is going to occur, and rectify the fault (either by fixing or replacing the motor) before the fault causes a motor failure and an unplanned downtime event.
The central tool behind Predictive Maintenance is Condition Monitoring. There are a number of different ways to monitor the condition of a motor, and the method that works for you will depend on a number of factors including type and number of motors that you have. Popular methods of Condition Monitoring include: Vibration Analysis, MCSA, Oil Analysis, Acoustic Analysis, Thermal Analysis, visual inspection and the use of accelerometers.
SAM4 by Semiotic Labs uses MCSA (Motor Current Signature Analysis) to detect motor faults up to 4 months in advance. To sign up for a SAM4 demo, click here.
Condition Monitoring is also often easily scalable. Using automation and algorithms, data from hundreds of motors can be collected and analysed, saving you having to manually analyse huge amounts of data yourself. When a fault is detected, the system can quickly alert your maintenance team as to which motor is faulty, the reason the motor is faulty, and how long you have until the fault causes the motor to fail.
How could Predictive Maintenance benefit your operation?
To find out how Predictive Maintenance could benefit your organization, talk to one of our consultants or book a SAM4 demo.