A significant Revolution in Maintenance: AI/IoT-Guided Predictive Maintenance that stops Downtime
Manufacturers in all industries know the key to better productivity is keeping the production equipment running smoothly without unexpected stops. Short stoppages often do occur, however, and longer downtime for repair or part replacement remains a risk.
To improve factory productivity, people tend to focus on improving the processing capacity from the equipment itself. But those short stoppages and sudden shutdowns are in fact more inconvenient. They create it hard to predict productivity, and it’s bothersome to deal with them. Potential breakthroughs in this region will be a key advantage of smart factories.
Applying IoT to prevent the slight delays that add up to cause downtime
To keep production lines running safely so that as productively as planned, inspection and maintenance are essential (Fig. 1). There’s no denying this also takes time, effort, money, and people, yet some countries have faced lack of maintenance personnel in recent years amid a declining birth rate and aging population. Also, as maintenance is linked to product value indirectly, a lot of companies would rather lessen the personnel involved.
Fig. 1: Streamlining inspection and maintenance may be a key benefit of smart factories
Production lines may stop for a variety of reasons, but short stoppages occur when various factors add up -slight delays across the line, or equipment malfunctioning or going slightly from sync. This multiplicity of factors makes short stoppages difficult to predict.
Recently, manufacturers have applied IoT to gather data on equipment operating conditions and worker movement, which may be understood instantly. Collection and big-data research into the data acquired has led to approaches for predicting short stoppages by referring to past instances. Anticipated short stoppages might be preventable by adjusting the operating conditions of relevant equipment or having more workers involved.
A paradigm shift from corrective maintenance and traditional preventive maintenance
By contrast, additional factors may require repair, if mechanical parts need replacing or rust, dirt or foreign matter becomes stuck, or warmer temperatures cause expansion. Changes that ultimately lead to failure are complex and happen gradually, making this failure hard to predict.
Preventive maintenance has traditionally involved careful regular inspection and replacing partially used consumable parts to avoid failure that needs repair. This method has presented two challenges. First, individual part variation means that failure can happen earlier than expected. Second, it appears wasteful to exchange perfectly good parts before the end of their useful life.
But in recent years, IoT and AI have enabled a more proactive stance through early detection of indications of failure. This is whats called predictive maintenance (Fig. 2). Potential indicators such as appearance, sound, pressure, heat, and vibration are collected as data from sensors on production equipment, and analysis techniques which include AI help detect signs of failure or malfunction ahead of time. With predictive maintenance, consumable parts may be used to their full potential. This permits an organized approach to maintenance and part orders.
Fig. 2: Distinction between preventive and predictive maintenance
Smart factories as alert to anomalies as seasoned engineers
At some long-established factories, seasoned engineers can sense equipment anomalies by sound, or by vibrations felt with their hands. This keen awareness has prevented short stoppages or equipment failure requiring repair.
There are fewer and fewer of these veteran engineers now, and as the birth rate declines inside a graying society, it's difficult to spread these skills and much less people to train. With smart factories, systems could be built for nonstop monitoring of production equipment without missing anomalies -a feat impossible to match conventionally at factories that depend on the abilities of some people.
Steady yield and quality from advances in sensors, communication modules, and batteries
Failure prediction isn't the only advantage of more complex data collection and analysis technologies with more accurate anomaly detection. We are able to also predict issues such as declining yield and quality. However, better anomaly detection requires manufacturers to gather enough high-quality data from suitable areas, that also requires more advanced sensors, communication modules, batteries, along with other aspects of IoT systems that collect the information (Fig. 3).
Fig. 3: Key components for higher anomaly detection accuracy in smart factories
To this end, these components must be made smaller, lighter, and much more energy-efficient, to ensure that installation requirements do not limit where data can be collected. The constituents must also have high environmental resistance and noise immunity to ensure stable operation even under tough factory conditions.
One development in recent years that is gradually becoming more popular as a power source for IoT devices is energy harvesting, which turns ambient energy sources such as light, temperature differentials, electromagnetic waves, or vibration into electric power. Looking ahead, oxide-based solid-state batteries that improve the environmental resistance of battery-powered IoT devices promise to grow areas where these devices are used.