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This ability to quickly adapt and learn has led to deep learning for predictive maintenance increasingly being used for automated anomaly detection, which is the process of identifying abnormal or unusual behavior or patterns in data that indicate a potential problem or failure. Understandably, running anomaly detection accurately and at scale requires a lot of data and a lot of different types of data to paint the most vivid picture of what’ s happening within a piece of equipment.
Some data that feeds deep learning includes:
Acoustic: Experienced mechanics will often listen to an engine in operation to diagnose potential issues. With digital ears, however, it is possible to listen to sound outside human hearing ranges. Using ultrasonic analysis means that errors can be detected much earlier and based on minute changes that human ears cannot identify.
Infrared: Infrared analysis is essentially a measure of temperature across a system. For data centers and facilities that operate within relatively tight thermal controls, the ability to automate temperature monitoring can not only identify issues with a server but also help pinpoint right down to the circuit board level a component that might be failing. Rather than replacing entire units, individual failing components can be replaced, massively reducing the cost of repairs. For data centers, this process can also act as a failsafe for temperature sensors on a circuit board, which can themselves fail under certain conditions.
Infrared analysis is essentially a measure of temperature across a system.
Fluid analysis: By examining data such as temperature, viscosity, contamination levels, and particle content in lubricants and coolant fluids, organizations can detect signs of wear, overheating, and potential mechanical failures. This allows them to prioritize maintenance for the most at-risk equipment. It is one of the best indicators for firms to predict which equipment must be attended to first.
Visual inspection: Cameras and AI image scanners enable near real-time inspections, though concerns remain about the accuracy of fully automated visual assessments. Despite these challenges, the sheer volume of inspections can improve overall reliability. Automated alerts can also prompt timely human intervention from maintenance teams.
As manufacturing accelerates ever faster towards what has been dubbed Industry 4.0, the push to leverage data across these different areas is resulting in the wide-scale deployment of sensors. Through the use of sensors, ML in manufacturing doesn’ t need a single, fixed location. Instead, it is possible to apply predictive maintenance to remote or distributed ecosystems such as vehicle fleets, industrial machinery, construction, power generation and grid management. Implementation might disrupt established workflows for the teams that manage these resources, but the gains are worth it.
Upfront investment, an ounce of prevention
The upfront costs of investing in ML might seem high, particularly if you have systems and processes in
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