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Fault diagnosis has played a vital role in industry to prevent operation hazards and failures. To overcome the limitation of conventional diagnosis approaches, which misclassify new types of faults into existing categories from training, a novel probabilistic diagnosis framework will be proposed in this paper for effective detection on new data categories. Gaussian mixture model (GMM) is applied for...
In general, one limitation in current diagnosis approaches is that they could only detect the existing types of faults, while not be able to detect new types of faults. It is difficult to know in advance all fault types and new types of faults may occur in industry. As such, effective detection and diagnosis on new types of faults are important. In this paper, a novel mixed soft&hard assignment...
Effective equipment fault diagnosis can assist to schedule the proper maintenance and reduce breakdown risks for realistic engineering systems. In this paper, a novel two-step precognitive maintenance framework is proposed to diagnose the equipment health conditions based on its real-time Condition Monitoring (CM). The synthetic minority over-sampling technique is implemented firstly to balance a...
Gait as a biometric feature that can be measured remotely without physical contact and proximal sensing has attract significant attention. This paper proposes to use con-volutional neural networks (ConvNets) and multi-task learning model(MLT) to identify human gait and to predict multiple human attributes simultaneously. In comparison to previous approaches, two novelty in our convolutional approach...
Effective prediction of unobservable degradation can assist to schedule preventive maintenance and reduce unexpected downtime for realistic industrial systems. In this paper, an extended time-/condition-based framework is proposed for the Probability Density Function (PDF) prediction of unobservable industrial wear. Furthering our earlier work of unobservable degradation estimation, a stage-based...
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