Governments are pressuring industries to reduce energy consumption for both environmental and economic reasons. Optimizing factory processes and improving equipment can lower energy usage but this not only takes time and money, it also requires a vast amount of background operational knowledge.
Now, Oon Peen Gan and co-workers at A*STAR’s Singapore Institute of Manufacturing Technology, together with researchers at the National University of Singapore and The University of Texas, United States, have developed an approach to track the daily energy usage of individual machines. Their approach monitors the operational state of a machine in real time1.
“Our proposed idea improves energy efficiency through better sequence control of machines and operations,” notes Gan. “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 molding 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 analyzing 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 analyze 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,” explains 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 molding 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,” says Gan. “We aim to validate the model with experiments at a number of industrial companies in Singapore in the near future.”
The A*STAR-affiliated researchers contributing to this research are from the Singapore Institute of Manufacturing Technology
Le, C. V., Pang, C. K., Gan, O. P., Chee, X. M., Zhang, D. H. et al. Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems. Transactions of the Institute of Measurement and Control 35, 583–592 (2013).
'Super yeast' has the power to improve economics of biofuels
18.10.2016 | University of Wisconsin-Madison
Engineers reveal fabrication process for revolutionary transparent sensors
14.10.2016 | University of Wisconsin-Madison
Researchers from the Institute for Quantum Computing (IQC) at the University of Waterloo led the development of a new extensible wiring technique capable of controlling superconducting quantum bits, representing a significant step towards to the realization of a scalable quantum computer.
"The quantum socket is a wiring method that uses three-dimensional wires based on spring-loaded pins to address individual qubits," said Jeremy Béjanin, a PhD...
In a paper in Scientific Reports, a research team at Worcester Polytechnic Institute describes a novel light-activated phenomenon that could become the basis for applications as diverse as microscopic robotic grippers and more efficient solar cells.
A research team at Worcester Polytechnic Institute (WPI) has developed a revolutionary, light-activated semiconductor nanocomposite material that can be used...
By forcefully embedding two silicon atoms in a diamond matrix, Sandia researchers have demonstrated for the first time on a single chip all the components needed to create a quantum bridge to link quantum computers together.
"People have already built small quantum computers," says Sandia researcher Ryan Camacho. "Maybe the first useful one won't be a single giant quantum computer...
COMPAMED has become the leading international marketplace for suppliers of medical manufacturing. The trade fair, which takes place every November and is co-located to MEDICA in Dusseldorf, has been steadily growing over the past years and shows that medical technology remains a rapidly growing market.
In 2016, the joint pavilion by the IVAM Microtechnology Network, the Product Market “High-tech for Medical Devices”, will be located in Hall 8a again and will...
'Ferroelectric' materials can switch between different states of electrical polarization in response to an external electric field. This flexibility means they show promise for many applications, for example in electronic devices and computer memory. Current ferroelectric materials are highly valued for their thermal and chemical stability and rapid electro-mechanical responses, but creating a material that is scalable down to the tiny sizes needed for technologies like silicon-based semiconductors (Si-based CMOS) has proven challenging.
Now, Hiroshi Funakubo and co-workers at the Tokyo Institute of Technology, in collaboration with researchers across Japan, have conducted experiments to...
14.10.2016 | Event News
14.10.2016 | Event News
12.10.2016 | Event News
21.10.2016 | Health and Medicine
21.10.2016 | Information Technology
21.10.2016 | Materials Sciences