A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection

Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.

Cascade of One Class Classifiers for Water Level Anomaly Detection

Intelligent anomaly detection is a promising area to discover anomalies as manual processing by human are generally labor-intensive and time-consuming. An effective approach to deal with is essentially to build a classifier system that can reflect the condition of the infrastructure when it tends to behave abnormally, and therefore the appropriate course of action can be taken immediately. In order to achieve aforementioned objective, we proposed to build a dual-staged cascade one class SVM (OCSVM) for water level monitor systems. In the first stage of the cascade model, our OCSVM learns directly on single observation at a time, 1-g to detect point anomaly. Whereas in the second stage, OCSVM learns from the constructed n-gram feature vectors based on the historical data to discover any collective anomaly where the pattern from the n-gram failed to conform to the expected normal pattern. The experimental result showed that our proposed dual-staged OCSVM is able to detect anomaly and collective anomalies effectively. Our model performance has attained remarkable result of about 99% in terms of F1-score. We also compared the performance of our OCSVM algorithm with other algorithms.