Failure in production is often fatal. If it’s monitored by humans, errors might be spotted to late or even not at all. Wherever humans are working in production, mistakes are inevitable: First humans can only focus on a few certain things. Especially for complex manufacturing processes, it can be hard to keep track of potential warning signs that forecast problems that could occur. Even if potential warning signs are registered by production workers, due to a lack of knowledge or experience they might not diagnose the (right) problem. A study performed by Vanson Bourne concludes that 23% of production losses and unplanned downtime in manufacturing are the result of human error. It was named the number one reason for production losses in manufacturing. Because of the variety of machinery and equipment that requires intervention and maintenance by service engineers and technicians, the manufacturing sector has higher levels of human error. As production gets more and more complex, error prediction and prevention becomes harder for humans. To fix that, real-time measurement data can be collected by a variety of sensors. This way factors relevant for maintenance e.g. vibration or optical deviations can be kept monitored.
Even if this kind of Data is captured, humans may have problems overlooking all this information. It can be hard to grasp all potential manufacturing problems or predict future failure implied by the data – simply because the Data can be overwhelming and patterns in the Date might be hard for humans to detect. The large amount of sensory input that can be monitored by AI. It can be used to cope with all the Data collected by the sensors that are relevant for maintenance. With all that information it can identify the state of machinery and equipment for manufacturing and also predict upcoming manufacturing problems. This way, AI can reduce the number of human errors in predicting what failure might occur and when it will happen – which also lowers maintenance costs. Its accuracy in interpreting the Data can help to improve the overall safety and efficiency of the manufacturing process.
The combination of INDAAQs sensory Systems can help gather the information relevant for the maintenance of your production. Our AI driven maintenance prediction can handle the amount of Data and monitor the condition of the manufacturing process, predict maintenance, detect production failure and even forecast the exact time when those things will occur.