MAY 22, 2022
Artificial intelligence in factory maintenance is no longer a matter of the future
APR 27, 2022
Undetected machine failures are the most expensive ones. That is why many manufacturing companies are looking for solutions that automate and reduce maintenance costs. Traditional vibrodiagnostic methods can be too late in many cases. Taking readings in the presence of a diagnostician occasionally may not detect a fault in advance. 2017 Position Paper from Deloitte (Deloitte Analytics Institute 7/2017) claimed that maintenance in the environment of Industry 4.0. The benefits of predictive maintenance are dependent on the industry or the specific processes that it is applied to. However, Deloitte analyses at that time have already concluded that material cost savings amount to 5 to 10% on average. Equipment uptime increases by 10 to 20%. Overall maintenance costs are reduced by 5 to 10% and maintenance planning time is even reduced by 20 to 50%! Neuron Soundware has developed a artificial intelligence powered technology for predictive maintenance.
Stories from companies that have embarked on the digital journey are no longer just science fiction. They are real examples of how companies are coping with the lack of skilled labor on the market. Usually mechanic-maintainer who regularly goes around all the machines and diagnoses their condition by listening to them. Some companies are now looking for new maintenance technologies to replace
A failure without early identification means replacing the entire piece of equipment or its part. Waiting for the spare part which may not be in stock right now. Because it is expensive to stock replacement equipment. Devaluation of the current pieces of the component in the production thus the discarding of the entire production run. Finally, yet importantly, it would represent up to XY hours of production downtime. The losses might run into tens of thousands of euros.
Such a critical scenario is not possible if the maintenance technology is equipped with artificial intelligence in addition to the mechanical knowledge of the machines. It applies this knowledge itself to the current state of the machine. It is also able to recognize which anomalous behavior is currently occurring on the machine. Based on that send the send the corresponding alert with precise maintenance instructions. Manufacturers of mechanical equipment such as lifts, escalators, and mobile equipment use this today, for example.
However, predictive maintenance technologies have much wider applications. Thanks to the learning capabilities of artificial intelligence, they are very versatile. For example, the technology is able to assist in end-of-line testing. For example to identify defective parts of produced goods which are invisible to the eye and appear randomly.
The second area of application lies in the monitoring of production processes. We can imagine this with the example of a gravel crusher. A conveyor delivers different sized pieces of stone into grinders, which are to yield a given granularity of gravel. Previously, the manufacturer would run the crusher for a predetermined amount of time. To make sure that even in the presence of the largest pieces of rock, sufficient crushing occurred. With the artificial intelligence “listening” to the size of the gravel. He can stop the crushing process at the right point. This means not only saving wear and tear on the crushing equipment but more importantly, saving time and increasing the volume of gravel delivered per shift. This brings great financial benefit to the producer.
When implementing predictive maintenance technology, it does not matter how big the company is. The most common decision criterion is the scalability of the deployed solution. In companies with a large number of mechanically similar devices, it is possible to quickly collect samples that represent individual problems. From which the neural network learns. It can then handle any number of machines at once. The more machines, the more opportunities for the neural network to learn and apply detection of unwanted sounds.
Condition monitoring technologies are usually designed for larger plants rather than for workshops with a few machine tools. However, as hardware and data transmission and processing get progressively cheaper, the technology is getting there too. So even a home marmalade maker will soon have the confidence that his machines will make enough produce, deliver orders to customers on time, and not ruin its reputation.
In the future, predictive maintenance will be a necessity. In industry also in larger electronic appliances such as refrigerators and coffee machines, or in cars. For example, we can all recognize a damaged exhaust or an unusual sounding engine. Nevertheless, it is often too late to drive the car safely home from a holiday. For example, without a visit to the workshop. With the installation of an AI-driven detection device, we will know about the impending breakdown in time and be able to resolve the problem in time, before the engine seizes up and we have to call a towing service.
Pavel is a tech visionary, speaker, and founder of AI and IoT startup Neuron Soundware. He started his career at Accenture, where he took part in 35+ technology and strategy projects on 3 continents over 11years. He got into entrepreneurship in 2016 when he founded a company focused on predictive machine maintenance using sound analysis.