The condition monitoring is a continuous process of monitoring of certain machine parameters related to the operation and structural condition of the machinery. It helps to judge whether the machines are in normal or deteriorating condition, which could prevent otherwise unforeseen damages. In order to retain or restore a machine to a specified operable condition or to achieve its maximum useful life, various maintenance strategies have been developed. The maintenance strategies mainly divide into three groups as shown in Figure 1.1.
Maintenance
Reactive Maintenance (Unplanned)
Preventive Maintenance
(Planned)
Condition Based Maintenance (Condition based)
Figure 1.1 Maintenance strategies
The reactive maintenance is the earliest maintenance technique. It is basically a breakdown maintenance, which takes place only at the breakdowns. It is also called unplanned maintenance, or run-to-failure maintenance. After that, another maintenance technique, i.e. time-based preventive maintenance was introduced, which sets a periodic interval to perform preventive maintenance regardless of the health status of a machine. It is also called planned or scheduled maintenance. However, with the rapid development of modern technology, products have become more and more complex while better quality and higher reliability are required. This makes the cost of preventive maintenance higher and higher. Eventually, the preventive maintenance has become a major expense of many industrial companies. Therefore, more efficient maintenance approaches such as the condition-based maintenance (CBM) also called predictive maintenance are being developed to handle the situation (Martin, 1994).
CBM is a maintenance program that recommends maintenance actions based on the information collected through condition monitoring. CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behaviors of a machine.
A CBM program, if properly established and effectively implemented, can significantly reduce maintenance cost by reducing the number of unnecessary scheduled preventive maintenance operations (Jardine et al. 2006; Randall, 2011). The procedure of CBM is as shown in Figure 1.2.
Data Acquisition (Data or signal
collection)
Data Processing (Signal analysis)
Decision Making (Fault diagnosis/
prognosis)
Figure 1.2 Components of the condition based maintenance
The data acquisition is a process of CBM of collecting and storing useful data or information from the targeted physical assets. The condition monitoring data are very versatile. It may be the vibration, current, acoustic, temperature, pressure, oil analysis data, etc., depending upon the machine. In order to acquire the data, various sensors such as the accelerometer, current probes, acoustic emission sensors, ultrasonic sensors, thermocouples, pressure sensors, etc. have been developed.
The data processing includes the process of feature extraction of different faults of a machine. The feature extraction is used to reduce the dimension of data by selecting the important features. The accuracy and effectiveness of signal processing techniques depend on the feature characteristics that can be obtained from the time, frequency, and time-frequency domain (Jardine et al. 2006).
The simplest form of signal processing is that the magnitude of the raw incoming signal is examined on a regular basis in the time domain. The signal processing in such cases may consist of a comparison of the current record with the previous value or with some preset or predetermined threshold. The earlier example of magnitude detection implies that the motor condition information is seeded in the change of signals obtained from the motor. The variation of signals usually tells us the change of condition. Many techniques can be used for time-domain analysis. Traditional time domain calculates characteristic features from time waveform signal as descriptive statistics such as the mean, peak, peak-to-peak interval, standard deviation, skewness, kurtosis, and higher order statistics, etc. These features are usually called time domain features. Time domain averaging (TDA) is a traditional and typical method to detect fault signals in rotating machines. It extracts a periodic component of interest from a noisy compound signal. Data-clustering techniques are used to extract an average pattern that serves as the mechanical imbalance indicator (Lin, 2009)
Frequency-domain analysis is more attractive than time domain analysis because it can provide more detailed information about the status of the machine. In the frequency domain, spectral analysis is a very useful technique of signal processing used in the fault diagnosis. Spectral analysis is effective when applied to steady-state periodic signals, which is usually the case with monitoring machine faults that gradually develop. To improve the data processing, methods based on fast Fourier transform (FFT), Hilbert transform (HT) and high resolution spectrum analysis have been applied (Mehrjou et al., 2011). The FFT is the most widely used technique in the frequency domain. The concept of FFT based techniques relies on analyzing the whole spectrum or identifying certain fault frequencies or harmonics related to a particular fault and thus extracting features from the signals. The nature of the signals of rotating machines may be stationary or non- stationary. For stationary signals, the FT provides an ideal candidate for feature extraction.
However, FT suffers from the problem of frequency band resolution and is not suitable for the transient and non-stationary signals. Many factors, including the change of the environment and the faults from the machine itself, often make the output signals (of the running motor) transient and non-stationary (Thomson and Orpin, 2002). These signals often contain inherent information of the faults that cannot be revealed by the traditional FFT based technique.
For non-stationary signals, features reflecting machine faults do not consist of regular frequency components with respect to time. They often demonstrate a transient nature, and carry small components embedded in larger repetitive signals. Thus, to handle the transient and non-stationary signals, and the problem of frequency resolution various time-frequency analysis have been developed such as the Wigner-Ville distribution (WVD), short-time Fourier transform (STFT), and wavelet transform (WT) (Peng and Chu, 2004). However, the WT is preferred over other time-
frequency analysis because it uses varying window (called mother wavelet) for different frequency signals that make it suitable for de-noising and extraction of weak signals, singularity detection, and, the system and parameter identification (Peng and Chu, 2004).
Three variants of WT, i.e. continuous wavelet transform (CWT), discrete wavelet transform (DWT) and wavelet packet transform (WPT) have been developed and used for the fault diagnosis.
The ability to examine the local behavior of the signal with reasonably precise frequency information is one of the most important features of the WT. Due to its good properties in time- frequency domain analysis the wavelet analysis has gained much attention from many engineering fields. The WT technique has been developed to reveal the hidden information that is not readily available in the raw signals (Chow and Hai, 2004; Antonino-Daviu et al. 2006). Whereas the CWT and DWT provide the signal analysis with more flexible time-frequency resolution, their drawback is that they cannot split the high frequency band where the modulation information of machine fault always exist. The expansion of classical WT, i.e. the WPT can overcome this difficulty of frequency resolution in the high frequency region (Yan et al. 2014).
The third step of CBM, i.e. the maintenance decision making is an essential step in implementing a CBM program for the machine fault diagnosis and prognosis. The diagnosis deals with the fault detection, isolation and identification when it occurs. The fault detection indicates whether something is going wrong in the machine; the fault isolation locates the machine component that is faulty; and the fault identification determines the severity of the fault when it is detected. The prognosis deals with the machine fault prediction before it occurs. Obviously, the prognosis is superior to diagnosis in the sense that it can prevent faults or failures, and if impossible, be ready
(with prepared spare parts and planned human resources) for the problems, and thus save extra unplanned maintenance cost. Nevertheless, the prognosis cannot completely replace the diagnosis since in practice, there are always some machine faults and failures that are not predictable.
Besides, the prognosis, like any other prediction techniques, cannot be 100% sure to predict faults and failures. In the case of unsuccessful prediction, the diagnosis can be a complementary tool for providing a maintenance decision support. In addition, the diagnosis is also helpful in improving the prognosis in the way that the diagnostic information can be useful for preparing more accurate event data and hence building better CBM model for the prognosis. Furthermore, the diagnosis information can be used as useful feedback information for the system redesign (Jardine, 2006).
Machine fault diagnostics is a procedure of mapping the information obtained in the measurement space and/or features in the feature space to machine faults in the fault space. This mapping process is also called the pattern recognition. Different conventional pattern recognition methods are available which can identify certain faults in machines, such as the power spectrum graph, phase spectrum graph, cepstrum graph, AR spectrum graph, spectrogram, wavelet scalogram, wavelet phase graph, etc. However, these techniques often present several problems in terms of complexity and cost. The conventional signal based methods are not always reliable as they depend over the operating conditions of the motors (Lee et al., 2006). In addition, other fault diagnosis methods are also available based on mathematical modelling of machines (Rong and Xiuhe, 2007). Based on the explicit model, residual generation methods such as the Kalman filter, parameter estimation (or system identification) and parity relations are used to obtain signals, called residuals, which is an indicative of the fault presence in the machine. Finally, the residuals are evaluated to arrive at the fault detection, isolation and identification. The model based diagnosis can be more effective
than other model free approaches, if accurate model is developed. However, several assumptions must be made to develop a mathematical model to take care of the nonlinear and stochastic machine dynamics, still it is not robust enough in the presence of perturbation and noise (Bilski, 2014). In addition, explicit modelling may not be possible and feasible for complex systems because it is not easy to consider the disturbances and the uncertainty in the models.
The conventional signal based method is more accurate and preferred than the model based method because it does not require any assumptions and complex mathematical models (Da-Silva et al., 2008). The machine fault diagnosis based on conventional methods, mathematical models require practice engineers to have sufficient knowledge and experience. As the number of rotating machines is increasing steadily in all types of industries, so it is not practical to fulfill the demand of required expertise. The increasing economic pressure requires the development of a cost- effective maintenance system to guarantee machine operating reliability and at relatively low cost.
Therefore, during the last decade, in order to automate, improve the reliability and sensitivity, and reduce the cost of the fault monitoring and diagnosis technique, a more sophisticated technique, called intelligent fault diagnosis, is developing and growing popularity in the field of mechanical engineering (Siddique et al. 2003).
The intelligent fault diagnosis is possible by incorporating artificial intelligence (AI) into the online machine condition monitoring. The AI based diagnoses have shown improved performance over the conventional signal and modelling based approaches. This reduces the direct human- machine interaction for the diagnosis. Moreover, these are data based techniques; therefore they do not require any detailed knowledge of the IM model and parameters. These techniques use
association, reasoning and decision making processes as would the human brain in solving diagnostic problems. These diagnostic techniques involve signal processing methods and classification tools such as the neural network (NN), fuzzy logic (FL), fuzzy neural network (FNN), genetic algorithm (GA), Hidden Morkov model, Bayesian classifier and SVM (Baccarini et al. 2011; Zhou et al. 2016).
The development of AI based diagnostic system in the machine condition monitoring is summarized here. For example, Chang et al. (1995) developed the on-line operator aid system (OASYS) to support the operator’s decision making process and to ensure the safety of a nuclear power plant by providing operators with proper guidelines in a timely manner, according to the plant operation mode. The OASYS uses a rule-based expert system and fuzzy logic. The rule- based expert system is used to classify the predefined events and track the emergency operating procedures (EOPs) through data processing, and the fuzzy logic is used to generate the conceptual high-level alarms for the prognostic diagnosis and to evaluate the qualitative fuzzy criteria used in the EOPs. Evaluation results show that the OASYS is capable of diagnosing plant abnormal conditions and providing operators appropriate guidelines with fast response time and consistency.
Liu et al. (1996) applied the fuzzy expert system for the fault detection in bearing. Ayoubi and Isermann (1997) gave an overview of integration techniques for the fuzzy expert system and the adaptive neural network. A special focus was given to develop a hybrid neuro-fuzzy network for monitoring the air pressure in vehicle tires. Mechefske (1998) applied fuzzy logic techniques to classify various rolling element bearing faults based on frequency spectra. Li et al. (2000) performed the fault diagnosis of rolling bearings of motors using neural networks and time/frequency-domain vibration analysis.
Qingling et al. (2001) used the fuzzy back propagation (BP) network to diagnose various faults in rotating machines (such as the unbalance, misalignment, hydraulic and aerodynamics forces, rotational disconnection, surge and film whirling motion). Huang and Huang (2002) developed evolving wavelet networks (EVNs) for the condition monitoring and fault diagnosis of power transformers. They compared the results with the fuzzy diagnostic system and the ANN, and concluded that the EWNs have a better capability for generalization than the fuzzy system and the ANN. Jack et al. (2002) illustrated the fault detection of roller bearings using the SVM and the ANN. They defined and estimated statistical features based on moments and cumulants, and selected the optimal features using the GA. Zhang et al. (2003) applied fuzzy neural networks (FNN) in fault diagnosis of rotary machines, especially to water pump sets of oil plants, and concluded that the FNN improves the recognition rate of the pattern recognition even when the sample data are similar.
Samanta et al. (2003) performed ANN based fault diagnostics of rolling element bearings using time domain features. In other work, Samanta et al. (2003) used ANN and SVM with genetic algorithm for bearing fault detection. Samanta (2004) performed the gear fault detection using the ANN and the SVM, and concluded that the SVM can perform well in comparison with the ANN even with the smaller number of samples and also the training time is less in case of SVM. Rojas and Nandi (2005) proposed the development of SVM for the detection and classification of rolling bearing faults. The training of SVM was done using sequential minimal optimization algorithm, and a mechanism for selecting adequate training parameter was also proposed. This makes the classification procedure fast and effective.
Widodo and Yang (2007) summarized and reviewed the recent research and developments of the SVM in the machine condition monitoring and diagnosis. They showed that various intelligent systems such as the ANN, fuzzy logic, condition based reasoning and random forest have been developed for the machine fault diagnosis; however, the use of SVM is rare in the same field. The SVM has excellent performance in generalization, so it can produce high accuracy in the classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in the machine condition monitoring and fault diagnosis was tending to develop towards the expertise orientation and problem-oriented domain. Finally, they concluded that the ability to continually change and obtain an idea for the machine condition monitoring and fault diagnosis using the SVM will have lots of scope in near future.
After that, in a work Hu et al. (2007) performed the fault diagnosis in rolling element bearings based on an improved WPT and the SVM ensemble, and achieved a high prediction rate even in the presence of noise. Yu et al. (2007) illustrated a fault diagnosis approach for roller bearing based on the intrinsic mode function (IMF) envelope spectrum and the SVM. Sui et al. (2009) illustrated rolling element bearings fault classification based on the feature evaluation with the SVM. The feature evaluation based on the class separatibility criterion was discussed in this work. Yang et al. (2007) applied a fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Sugumaran et al. (2008) presented fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Xian and Zeng (2009) performed an intelligent fault diagnosis of the rotating machinery based on the WPT and the SVM. In, addition, they compared the performance of the hybrid SVM and the back-propagation (BP) network. They
concluded that the accuracy of SVM is better than those of the BPN, and the SVM spent less time than the BPN in classification.
Meng et al. (2010) performed a comparison between the RBF neural network and the SVM for cases where only limited training samples were available for the fault diagnosis of rolling bearing.
The result showed that the SVM had better performance than the RBF neural network both in the training time and the prediction accuracy. Saravanan and Ramachandran (2010) performed incipient gear box fault diagnosis using DWT and ANN. Sugumaran et al. (2010) illustrated the effect of number of features on the classification of roller bearing faults using the SVM and the proximal support vector machine (PSVM). A set of statistical features and histogram features were extracted from time domain signals and the order of importance was found using decision tree. In other work, Kankar et al. (2011) presented fault identification in ball bearings based on the CWT and the AIs (i.e. the ANN, Self-Organizing Map (SOM) and SVM). Six different wavelets were considered in the study. Finally, they concluded that Meyer and complex Meyer wavelet with the SVM gives the best performance for the bearing fault diagnosis.
Li et al. (2012) successfully used the WPT feature and the SVM for the fault diagnosis of the gearbox and gasoline engine valve trains. Zheng et al. (2012) illustrated the rolling element bearing fault diagnosis based on the SVM. In this paper, the wavelet packet analysis was used to extract the features from the vibration signal and the principal component analysis (PCA) was performed for features reduction. Liu et al. (2012) illustrated the multi-fault classification based on the wavelet SVM (WSVM) with the particle swarm optimization (PSO) algorithm to analyze vibration signals from rolling element bearings. Li et al. (2013) performed the fault diagnosis of rolling
element bearings by the SVM. Improved ant colony optimization (IACO) algorithm was used for the optimization of SVM parameters. Zhu et al. (2014) illustrated the roller bearing fault diagnosis method based on the hierarchical entropy and the SVM with the PSO algorithm. Gangsar and Tiwari (2014) successfully applied SVM for bearing fault diagnosis at interpolated speed and extrapolated speed using vibration signals. Bordoloi and Tiwari (2014) performed the optimum multiclass classification of gears with integration of the evolutionary and SVM algorithms. In another work, Bordoloi and Tiwari (2014) attempted the fault classification of gears using wavelet based features and the SVM. The genetic algorithm, grid-search method and artificial bee colony algorithms were used for optimizing SVM parameters.
As the condition monitoring and diagnosis of rotary machines has moved from traditional techniques to AI techniques, so there is much scope of research in this field. The AI based diagnostic systems still have several challenging tasks to accomplish in regards to its efficiency, reliability, computational time, sufficient database, and robustness. Nowadays, the SVM is extensively gaining popularity in machine fault diagnosis owing to their best prediction performance, and less training and testing time. In addition, SVM has advantages of handling large amount of data with several classes. It can effectively classify this data into classes using feature spaces. The best property of SVM algorithms is that it can perform well even with the small number of training and testing data; hence, reduces the computational load. The basics of SVM have been developed by Vladimir Vapnik in 1994, which is based on binary class classification.
Later, the SVM has been extended to the multiclass-classification from the binary class classification in order to classify the multi-fault in machines (Hsu and Lin, 2002). In order to compare multiclass SVM techniques, Hsu and Lin (2002) and Hsu et al. (2003) presented a