# An Intelligent Condition Monitoring Approach for
## 1 Introduction
The wavelet packet transform (WPT), which is a further development of WT, can improve the signal's time-frequency resolution by decomposing the frequency bands into several levels adaptively, especially high-frequency bands that the WT cannot finely decompose [17]. Hence, WPT is more suitable to analyze nonlinear and non-stationary signals, and it was selected for feature extraction of the health states regarding the shearing machine in this study. In recent years, data-based models have been playing an increasingly important role in fault diagnosis [18; 19; 20; 21]. Intelligence methods, such as expert system (ES), artificial neural network (ANN), hidden Markov model (HMM) and support vector machine (SVM) have been widely applied in the fault diagnosis of mechanical equipment [22; 23; 24; 25]. HMM, a statistical model of time series, which was founded in the 1970s, was initially applied for speech recognition, and now has become an important branch in the field of signal processing [26]. It is well known for its applications in reinforcement learning and temporal pattern recognition such as speech, handwriting and gesture recognition. Meanwhile, HMM has been introduced into mechanical monitoring, e.g., cutting tools, bearings, engines and pumps, etc., and even the fault identification of large and complex systems, such as distributed sensor networks [27; 28; 29]. The advantage of HMM lies in its dynamic-modeling ability regarding time series. Therefore, it is especially suitable for temporal pattern recognition. For common classification, it is not necessarily better than other methods. A signal contaminated by noise may be misjudged, i.e., the anti-interference ability of HMM is not strong enough. If the identified signal is contaminated by noise, it may be misjudged, i.e., the anti-interference ability of HMM is not strong [30]. ANN is a kind of computing system inspired by the biological neural systems of animals. As is known, ANN has strong self-adaptability, learning ability and nonlinear mapping ability as well as robustness and fault tolerance [31]. At present, it has been widely used in image recognition, speech recognition, machine translation and other fields, and become the most active field of artificial intelligence research. Due to the aforementioned advantages and its insensitivity to incomplete or characteristic defects of input information, the ANN-based methods are suitable for online fault diagnosis [32; 33]. Some researchers combined ANN with self organizing mapping (SOM), particle swarm optimization (PSO), chaos, or rough sets theory to solve some complicated pattern recognition issues [34; 35]. A study shows that the hybrid model combining HMM and ANN is more effective than the isolated HMM for speech emotion recognition [36]. In practice, noise signals acquired from an operating shearing machine have the characteristics of non-stationary, poor reproducibility and less accumulated samples, but they contain a lot of information about the health states of the shearing machine. Considering the discussion above, an intelligent condition monitoring approach for spent fuel shearing machines consisting of WPT-based feature extraction hybrid HMM-ANN based fault detection steps was proposed in this paper. In this approach, to combine the advantage on the capability to dynamic modeling of HMMs and pattern recognition of ANN, the feature vectors extracted from the noise signals are fed to the trained HMMs to obtain the likelihoods, which are used as the input of ANN, while ANN is used to make a decision.