V. Sugumaran

Fault diagnosis of bearings through vibration signal using Bayes classifiers

Bearings are an inevitable part in industrial machineries, which is subjected to wear and tear. Breakdown of such crucial components incur heavy losses. This study concerns with fault diagnosis through machine learning approach of bearing using vibration signals of bearings in good and simulated faulty conditions. The vibration data was acquired from bearings using accelerometer under different operating conditions. Vibration signals of a bearing contain the dynamic information about its operating condition. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The decision tree has been formulated using J48 algorithm. The selected features were then used for classification using Bayes classifiers namely, Naive Bayes and Bayes net. The paper also discusses the effect of various parameters on classification accuracy.

A comparative study of naïve Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal

Bearing is an important and necessary part of any big or small machinery and for proper working of machinery the bearing condition should be good. Hence, there is a requirement for continuous bearing monitoring. For the condition monitoring of bearings sound signal can be used. This paper uses sound signal for condition monitoring of roller bearing by naive Bayes and Bayes net algorithms. The statistical features from the sound signals were extracted. Then features giving better results were selected using J48 decision tree algorithm. These selected features were classified using naive Bayes and Bayes net algorithm. The classification results for both naive Bayes and Bayes net algorithm for fault diagnosis of roller bearing using sound signals were compared and results were tabulated.

Fault diagnosis of helical gear box using naïve Bayes and Bayes net

Gears are one of the vital transmission elements, finding numerous applications in small, medium and large machinery. The vibration signals of a rotating machine contain dynamic information about its health condition. There are many articles in the literature reporting the suitability of vibration signals for fault diagnosis applications. Many of them are based on FFT, and have their own drawbacks with non-stationary signals like the ones from gears. Hence, there is a need for the development of new methodologies to infer diagnostic information from such signals. This paper uses the vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through the machine learning approach. A vibration-based condition monitoring system is presented for the helical gear box as it plays a relatively critical role in most of the industries. This approach has mainly three steps, namely, feature extraction, classification, and comparison of classification. This paper presents the use of the naive Bayes algorithm and Bayes net algorithm, for fault diagnosis through statistical features extracted from the vibration signals of good and faulty components of the helical gear box.