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Real-time energy audit cuts power use of factories

Posted: 28 Oct 2013 ?? ?Print Version ?Bookmark and Share

Keywords:manufacturing? energy consumption? finite-state machine?

"Our proposed idea improves energy efficiency through better sequence control of machines and operations," stated Oon Peen Gan, who led the study. "It can be as simple as switching off a light when not in use."

To test their idea, Gan and his team identified the operational state of two individual industrial moulding machines, based on their energy consumption. The researchers placed sensors inside the machines and fed the data from the sensors into a mathematical model called a finite-state machine (FSM), which is commonly used for analysing manufacturing processes. Since a machine in the 'start-up' state has a different energy output to one in full production, the FSM could be used to produce power-consumption profiles of the machines.

The researchers then used a unique two-stage framework to help them analyse and classify the data. "During the first stage we cleaned the raw energy signals using a digital filter to produce a much smoother dataset with less noise," explained Gan. "Secondly, we trained a pattern-recognition algorithm, or neural network, to classify the data into separate events. Each event represents a machine operation state."

Using the model, Gan and co-workers determined the exact operational state of each moulding machine in real time. Because the researchers could easily find abnormal energy patterns in the model output, the software tool may prove very useful for engineers looking for machine faults across the factory floor.

With the trained neural network in place, a software user can classify any machine's operational state from its energy output without needing to know the machine type. Theoretically, the model could be used to monitor many different types of machines in any industry.

"We hope to incorporate our new model into existing software that is used by manufacturers to monitor their shop floors," noted Gan. "We aim to validate the model with experiments at a number of industrial companies in Singapore in the near future."





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