Peter W. Tse

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This project investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies and trend output classification probabilities as a way to monitor the health of the system. In absence of " unhealthy " (negative) information, the marginal kernel density estimate of the " healthy " (positive) distribution is used to(More)
The fault diagnosis of rotating machinery has attracted considerable research attention in recent years because such components as bearings and gears frequently suffer failure, resulting in unexpected machine breakdowns. Signal processing-based condition monitoring and fault diagnosis methods have proved effective in fault identification, but the revelation(More)
In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this(More)
The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the(More)
Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this(More)
Failure of equipment not only leads to loss of production, but also, in some serious situations, causes human casualty. Hence, the need of equipment condition monitoring becomes crucial for reliable operations. Since expensive hardware instruments are needed for condition monitoring, with the current powerful PCs, software based virtual instruments are(More)
A vibration signal collected from a complex machine consists of multiple vibration components, which are system responses excited by several sources. This paper reports a new blind component separation (BCS) method for extracting different mechanical fault features. By applying the proposed method, a single-channel mixed signal can be decomposed into two(More)