10 April, 2018
How M2M enables predictive maintenance
When operating a business, there are costs associated with the assets (such as machines or other devices) that you will be using. Part of these costs are associated with maintenance, which can increase rapidly if assets are not properly maintained. Thankfully, as technology has advanced, so has the way people do maintenance, reducing the costs associated with keeping your machines up and running.
Traditionally, organisations performed corrective maintenance on a machine or device that had broken down or had become faulty. The purpose of this type of maintenance would be to repair the device so that it could be returned to operation. Corrective maintenance could work out to be expensive for the company due to costs associated with the potential downtime as well as the acquisition of new parts and repairs. The two types of corrective maintenance that companies can undertake are immediate corrective maintenance and deferred corrective maintenance.
Preventative maintenance is another type of maintenance and is done to avoid the problems associated with equipment failure by scheduling downtime. This scheduled downtime is used to identify potential issues and repair them. If the problem cannot be repaired at the time, additional time for maintenance can be scheduled before the machine or device fails completely. This type of maintenance is more cost-effective than corrective maintenance, and allows businesses to monitor depreciation and quickly address a malfunction if it occurs.
Many organisations today make use of predictive maintenance. Predictive maintenance is a system developed in order to "predict" when a device could potentially require repairs due to a failure. This offers a variety of benefits:
- Reduction in maintenance costs: 25% to 30%
- Elimination of breakdowns: 70% to 75%
- Reduction in equipment or process downtime: 35% to 45%
- Increase in production: 20% to 25%
(Source: M2M Telematics & Predictive Analytics)
Predictive maintenance is possible thanks to M2M sensors that gather data from the asset, usually in real-time, so that machine operators can monitor its performance.
These sensors can gather data from a variety of diagnostic tests such as:
- Acoustic analysis - This method of testing uses sound waves to identify gas or liquid leaks.Oil analysis - This method is used to detect wear in the device, oil degradation, oil contamination, and a variety of other issues.
- Vibration analysis - This method uses a transducer to pick up vibrations from bearings to detect performance degradation in devices such as pumps and motors.
- Infrared scans - Infrared cameras are used to monitor the temperature of the asset to ensure it is within tolerance levels.
These tests provide machine operators with critical information about the health of the machine, allowing for a more holistic approach to maintenance when compared to corrective or preventative maintenance.
Steve Hilton describes how M2M enables predictive maintenance using a three solution enhancement:
- Sensors gather data to monitor assets - M2M sensors on an asset continuously monitor the machine or device to ensure it is operating within acceptable tolerances. If these M2M sensors pick up changes that are outside of these tolerances, a warning is sent to the machine operators who will take action accordingly.
- Data is communicated along a suitable channel - The information being gathered by the M2M sensors is transmitted to a platform that is connected by a Wide Area Network (WAN) or LAN (Local Area Network), depending on the device. Some devices may be remote and hard to access directly, and will require a wireless WAN solution. Others will be easily accessible and can be directly connected to a wired LAN solution. Other factors that can affect the choice of channel include battery life, security protocols and device mobility.
- Use data analysis to create a predictive maintenance schedule - Data that is captured by the sensors will be analysed. Trends for successful operation are identified to create a set of rules for normal operation. Patterns will also be identified for when a device is about to fail. This information will be used to create a predictive schedule for when maintenance should occur as wear and tear occurs on the device. This will reduce the amount of unexpected downtime due to machine failure, as well as the costs associated with it.
M2M connectivity and predictive maintenance are vital in providing a business with information regarding the health of its machinery, and avoiding costly downtime when a problem is missed or undiscovered during the other types of maintenance.