Fault diagnosis of rolling bearing using CVA based detector

There are two key problems in bearing fault diagnosis that need to be addressed, one is feature selection, the other is faulty dataset problem. On the one hand, signal decomposition methods are popular ways to decompose signal into a number of modes of interest, while the most interesting modes need to be selected to represent original signal. This procedure may easily lead to loss of important information. On the other hand, most of works adopt the faulty data to train fault diagnosis classifier, while the faulty data sets are difficult to collect in real life. Hence many existing methods are unsuitable for practical application. Moreover, a high number of researchers introduce various hybrid methods to improve the ability of original methods, which increases the complexity of fault diagnosis. To solve these problems, firstly, a canonical variate analysis (CVA) detector based on visual inspection is proposed to classify operating states. Healthy dataset obtained under normal condition is applied for building a reference model and generating a threshold. CVA transforms the unknown variable into state space and residual space, then T2 and Q metrics are used to capture the variation in the two spaces, respectively. The metrics of variable compared with reference model will determine the state of rolling bearing. Considering that the threshold of proposed detector is likely to be exceeded, and visual inspection fails to identify bearing fault automatically. Then the means of T2 and Q metrics are presented to enlarge the distance between normal and abnormal conditions to avoid those drawbacks. At last, experiment and comparison are conducted to verify the capability of the proposed work. The results demonstrate that the proposed work is simple and effective in bearing fault diagnosis

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method

An integrated combined methodology for the outline gas turbines performance-based diagnostics and signal failure isolation.

The target of this research is the performance-based diagnostics of a gas turbine for the online automated early detection of components malfunctions with the presence of measurements malfunctions. The research proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high rate of success for single and multiple failures with the presence of measurement malfunctions – measurement noise. A combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman Filter has in his strength the measurement failure treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile, the neuro-fuzzy logic the estimation precision, used for the quantification and the fuzzy logic the categorization flexibility, which are used to classify the components failure. All contributors are also a valid technique for online diagnostics, which is a key objective of the methodology. In the area of gas turbine diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This research investigates the key contribution of each component of the methodology and reaches a success rate for the component health estimation above 92.0% and a success rate for the failure type classification above 95.1%. The results are obtained with the first configuration, running with the reference random simulation of 203 points with different level of deterioration magnitude and different combinations of failures type. If a measurement noise 5 times higher than the nominal is considered, the component health estimation drop to a minimum of 70.1% (reference scheme 1) while the classification success rate remains above 88.9% (reference scheme 1). Moreover, the speed of the data processing – minimum 0.23 s / maximum 1.7 s per every single sample – proves the suitability of this methodology for online diagnostics. The methodology is extensively tested against components failure and measurement issues. The tests are repeated with constant simulations, random simulation and a deterioration schedule that is reproducing several months of engine operations.PhD in Aerospac