# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Feature-Based (Signal Processing) Approach and Wavelet and Model-Based Approaches  
Complex structures and/or systems often undergo damage/failure profiles that require non-stationary analysis techniques in order to be capable of detecting them [51; 52; 53]. It is of critical interest to monitor these dynamic signals from inherent structural energy changes and vibration that reflect due to the damage/failure itself. Traditional Fourier series analysis models are not appropriate since they require stationary data. Multi-scale, time-frequency analysis techniques such as wavelets can be applied to provide a robust framework for efficient analysis of such non-stationary processes [54; 55]. Wavelet transforms have been studied since the 1950s by mathematicians, but it has only been in the last 10 years that they have made huge advancements in the engineering and signal processing community [6]. The wavelet transform is an operation that transforms a signal of interest with scaled and delayed from original signal to threshold signal [51]. Wavelet transformations are used for time-frequency representation of the signals for uncertainly/non-stationary using respective sampling theory. In signal monitoring and analysis, wavelet is also a very useful tool for failure prediction. On the basis of the signal from the sources, the different models could be realized for failure prediction. Wavelet and model/feature-based approaches are commonly used for failure prediction/condition-based monitoring and maintenance on the basis of the sensor and/or controller data. In another application of wavelet, an innovative scheme for the machinery condition monitoring was presented on the basis of the wavelet modulus maxima representation [56]. Signal decomposition technique is applied to extract gear motion signal, and then wavelet transform modulus maxima is utilized to define fault growth parameter (FGP). CBM with the vibration management enhancement program is used by the U.S. Army National Guard, U.S. Army Special Operations, and U.S. Army TMDE demonstration program [57]. In another study, Wang et al. developed robust health evaluation of gearbox for tooth failure with wavelet decomposition. A study was conducted for CBM in punching-blanking of sheet metal using statistical, artificial intelligence (AI), and model-based approaches [58; 59]. Special attention was given to inherent assumptions and other sources of inaccuracy, demonstrating how the signature of the force-displacement relation changes significantly with increasing tool wear in a typical configuration of sheet steel blanking. Kuravsky et al. [60] shows capability of wavelet transforms including relaxation neural networks for technical diagnostics and monitoring. Different types of signal processing are also used for data processing from the sensor system. A comparative study of various classification algorithms was carried out for fault diagnosis of electric motors using different types of signals [61]. Experimental evaluation with the relative performances of five classifiers using five types of steady-state signals were conducted on the basis of three kinds of performance evaluation strategies: training-test, cross-validation, and similar measure. The raw signals are collected, and features are extracted from the collected signals; the extracted features are classified using the five classification algorithms, and an overall comparison of the five classifiers is discussed. In another study, a multi-criteria decision model is presented to determine inspection intervals of condition monitoring based on delay time analyses to simultaneously determine inspection intervals for condition monitoring regarding the failure behavior of equipment to reduce cost and downtime [62]. A decision model application in an electric power distribution company was presented. This application highlighted the suitability and practicality of the model.