15.9 The State-of-the-Art on the Condition Monitoring
Condition monitoring of machines is gaining importance in industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. The use of vibration and acoustic emission (AE) signals is quite common in the field of condition monitoring of rotating machinery. By comparing the signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, rotor rub, shaft misalignment, gear failures and bearing defects is possible. These signals can also be used to detect the incipient failures of the machine components, through the on-line monitoring system, reducing the possibility of catastrophic damage and the down time. Some of the recent works in the area are listed in (Shiroishi et al., 1997; McFadden, 2000; Nandi, 2000;  Randall, 2001; Al-Balushi, and Samanta, 2002; Antoni and Randall, 2002; McCormick and Nandi, 1997; Dellomo, 1999). Although often the visual inspection of the frequency domain features of the measured signals is adequate to identify the faults, there is a need for a reliable, fast and automated procedure of diagnostics.

Artificial neural networks (ANNs) have been applied in automated detection and diagnosis of machine conditions (Nandi, 2000;  McCormick and Nandi, 1997; Dellomo, 1999; Samanta, and Al-Balushi, 2001 and 2002) treating these as classification or generalisation problems based on learning pattern from examples or empirical data modelling. However, the traditional neural network approaches have limitations on generalisation giving rise to models that can overfit to the training data. This deficiency is due to the optimisation algorithms used in ANNs for selection of parameters and the statistical measures used to select the model. Recently, support vector machines (SVMs), based on statistical learning theory, are gaining applications in the areas of machine learning, computer vision and pattern recognition because of high accuracy and good generalisation capability (Burges, 1998; Guyon and Christianini, 1999; Scholkopf, 1998; Gunn, 1998; Vapnik, 1999). The main difference between ANNs and SVMs is in the principle of risk minimisation (RM) (Gunn, 1998). In case of SVMs, structural risk minimisation (SRM) principle is used minimising an upper bound on the expected risk whereas in ANNs, traditional empirical risk minimisation (ERM) is used minimising the error on the training data. The difference in RM leads to better generalisation performance for SVMs than ANNs. The possibilities of using SVMs in machine condition monitoring applications are being considered only recently (Nandi, 2000; Jack and Nandi, 2000). In (Jack and Nandi, 2000), a procedure was presented for condition monitoring of rolling element bearings comparing the performance of two classifiers, ANNs and SVMs, with all calculated signal features and fixed parameters for the classifiers. In this, vibration signals were acquired under different operating speeds and bearing conditions. The spectral data and the statistical features of the signals, both original and with some preprocessing like differentiation and integration, low- and high-pass filtering were used for classification of bearing conditions. However, there is a need to make the classification process faster and accurate using the minimum number of features which primarily characterise the system conditions with an optimised structure of ANNs and SVMs (Nandi, 2000; Jack and Nandi, 2000). Genetic algorithms (GAs) were used for automatic feature selection in machine condition monitoring (Nandi, 2000; Jack and Nandi, 2002; Samanta et al., 2001). In (Jack and Nandi, 2002), the procedure of Jack and Nandi (2000) was extended to introduce a GA-based approach for selection of input features and classifier parameters, like the number of neurons in the hidden layer in case of ANNs and the radial basis function (RBF) kernel parameter, width, in case of SVMs. The features were extracted from the entire signal under each condition and operating speed. In Samanta et al. (2001), some preliminary results of ANNs and GAs were presented for fault detection of gears using only time-domain features of vibration signals. In this approach, the features were extracted from finite segments of two signals: one with normal condition and the other with defective gears. Tiwari et al. (2009) applied SVM to the fault identification in gears. They obtained excellent accuracy in prediction of fault or no-fault conditions, with some success on the multi-class fault diagnosis.

Final Remarks
To summarise, in the present chapter based on the vibration measurement the condition monitoring of rotating machinery has been described. Basic frequency components which may appear in the vibration signal due to various fault conditions have been described. Some basic fault conditions like the unbalances, misalignments, cracks, rubs, loose components, broken bar in induction motor, mechanical and electrical unbalance in induction motor, etc. have been discussed in detailed. Some basic rotating machine element fault conditions like gears, bearings, coupling, induction motor, etc. have been described. The method presented in the chapter for detecting the fault condition is very primitive; however, easy and effective in most of the practical cases, however, it involves human (expert) judgments. The subsequent chapter, deals with the active control of rotor by magnetic bearing by monitoring the vibration of the rotor, which is the main machine element gives dynamic forces to the support structures.