Automated Diagnosis of Faults in Rotating Machinery using Adaptive Network Based Fuzzy Inference. The long-term integrity of engineering assets depends on the quality of their maintenance which runs into billions of dollars per year in Australia. This project aims to develop a new fundamental automated technique for the detection and diagnosis of machinery faults. The innovation lies in the ability of this technique to not depend on knowledge of fault components in the discrete wavelet packet ....Automated Diagnosis of Faults in Rotating Machinery using Adaptive Network Based Fuzzy Inference. The long-term integrity of engineering assets depends on the quality of their maintenance which runs into billions of dollars per year in Australia. This project aims to develop a new fundamental automated technique for the detection and diagnosis of machinery faults. The innovation lies in the ability of this technique to not depend on knowledge of fault components in the discrete wavelet packet analysis. All other work conducted to date depends on knowledge of these components and their location. The results of this work will vastly improve the costly manually based diagnostics procedures in the maintenance of plant and industrial assets.Read moreRead less
Artificial intelligent system for integrated wear debris analysis and vibration analysis in machine condition monitoring. Vibration and wear debris analyses are the two main condition monitoring techniques for machinery maintenance and fault diagnosis. However, they can diagnose less than 50% of faults. A series of experimental and theoretical studies on the correlation of the two techniques will be conducted. This project will integrate advanced technologies including 3D microscopy, neural netw ....Artificial intelligent system for integrated wear debris analysis and vibration analysis in machine condition monitoring. Vibration and wear debris analyses are the two main condition monitoring techniques for machinery maintenance and fault diagnosis. However, they can diagnose less than 50% of faults. A series of experimental and theoretical studies on the correlation of the two techniques will be conducted. This project will integrate advanced technologies including 3D microscopy, neural networks and expert systems to develop an artificial intelligent system based on the dependent and independent roles of the two condition monitoring techniques. Successful outcomes will result in an improved maintenance program and reduction in human involvement, and will provide significant economic benefit to engineering industries.Read moreRead less