The manufacturing industry has always been fundamental to Taiwan's economic prosperity. However, the Made in China 2025 initiative and manufacturers being urged to return to the US have begun to worry the Taiwan manufacturing industry, which could be replaced by its counterparts in China and South Asia in five years if it fails to upgrade. Industry 4.0 smart manufacturing promises to enhance their core competitiveness.
Advancing technological developments such as artificial intelligence (AI), big data analytics and online/offline integration all fall within the scope of Industry 4.0 and are being put to use in wide-ranging applications. Among them, equipment monitoring technology can produce immediate results for the Taiwan manufacturing industry. Production equipment is instrumental to manufacturing. Much like a craftsman who can to do the job only with a good tool, manufacturers need their production equipment to maintain normal operation to keep their production lines up and running smoothly.
Health checks for production equipment used to be done through routine services, during which machines are halted for inspection, maintenance and replacement. Despite such routine services, manufacturers are still uncertain about the reliability of their production equipment as there is still no guarantee that the equipment will be fail-proof after the services.
Production equipment does not talk to operators to tell them when it is operating normally and when it is going to fail, so it is usually a disaster if downtime occurs due to equipment failure. Engineers will have to rush to the site to conduct checks and emergency repair. It is a race against time because equipment shutdown also means production halt with far-reaching consequences.
As such, the question is whether there is a preemptive way to solve the problem before a failure occurs and results in production line shutdown. This is exactly the job for equipment monitoring. Different from traditional time-based maintenance, equipment monitoring is all about using real-time status checks to constantly stay aware of equipment conditions during operation. Much like a doctor using diagnostic instruments to check a patient's physiological data, equipment monitoring makes use of various types of sensors to gather equipment health data such as electric current, vibration and noise to determine if it is functioning normally.
Big data analytics is then used to help operators predict the equipment's service life based on its actual operating status. Factory management can thereby make informed decisions according to actual findings collected during equipment inspections. Furthermore, Big data analytics can assist in the prediction of potential failure risks so that operators can make a preemptive response by adjusting production lines before the failure actually happens to prevent the production from being halted due to unexpected failure and thus enhance production stability.
As a matter of fact, equipment monitoring can also be valuable to people's everyday life. Take vehicle belts for example. They will show signs of wearing or aging over time. Unlike other automobile parts, they must be replaced before breakdown. A broken belt could pose serious threat to road safety so automakers generally request that car owners regularly check and replace vehicle belts.
A vehicle belt that does not show cracks does not necessarily mean there is no problem. Whether the belt needs to be replaced is to be determined by the mechanic based on his experience rather than scientific evidence. But this is hardly a safe practice.
In fact, how fast a belt wears out depends largely on how the driver uses the car. However, it may be difficult to track each driver's use habit so whether to replace a belt can rely on either the mechanic's experience or routine maintenance. From another perspective, if the belt is only mildly worn, routine check and replacement incurs extra costs to the car owner.
If there is a way to objectively assess the wear and health conditions of the belt, rather than relying on individual mechanics' experience, the car owner may be able to take the car to maintenance at more precise timings. The concept applies to manufacturing equipment monitoring as well. However, to a manufacturer, when production equipment failure occurs, it is not as easy as changing a belt.
For instance, should any equipment failure or unexpected emergency happen on a semiconductor manufacturing line that is supposed to operate around the clock, resulting in production shutdown, it could result in a multi-million-dollar loss. Aside from the loss of scrapped materials, foundries sustain even more pressure for failing to meet delivery schedule and stand to lose customers' trust.
Equipment maintenance is necessary. The worst case scenario is when unexpected failure occurs and emergency repair is needed. As this is unpredictable, it is difficult to estimate the downtime required for repair and the potential loss. Time-based maintenance is a safer approach but inevitably more costly.
Predictive and preventive maintenance made possible through equipment monitoring allows manufacturers to stay aware of real-time equipment operating conditions and foresee potential equipment failure. This is no doubt the most practical approach meeting the needs by smart manufacturing toady.
