# An Intelligent Condition Monitoring Approach for
## 1 Introduction  
Closed nuclear fuel cycles and spent-fuel reprocessing can effectively improve uranium resource utilization and reduce high-level radioactive waste. These methods are selected by most major nuclear-power countries [1; 2; 3]. A spent-fuel shearing machine, which is used for separating the fuel assembly seat from the fuel rods and to shear the latter, is the key piece of equipment for the first stage of spent-fuel reprocessing. The shearing machine may fail during operation due to, e.g., tool wear or damage. Once a failure happens and is not detected and treated in time, the whole spent-fuel reprocessing process will be affected. This can lead to serious accidents. Thus, it is essential to monitor the health state of the shearing machine and diagnose faults immediately. Owing to extreme harsh working conditions--e.g., strong radioactivity, acid gases, dust, no lubrication, and no permission to install contact sensors or stop the running--conventional monitoring methods (using, e.g., video, vibrations, and lasers) are not suitable for monitoring the state of a spent-fuel shearing machine [4; 5]. It is necessary to introduce new and intelligent approaches for fault diagnosis and health-state monitoring. It is discovered that noise signals produced by a shearing machine contain substantially useful information on the health state of the shearing machine [6]. In contrast to vibration-based methods, noise-based methods (with the advantage of not needing any direct contact) are very suitable for the fault diagnosis of shearing machines applied for spent-fuel reprocessing. Usually, noise-based fault diagnosis methods include acquiring noise signals of running equipments, extracting features from the noise signals, and making decisions through pattern recognition technology. Therefore,the key points of noise-based fault diagnosis methods are selecting the feature extraction method and determining the pattern recognition model [7]. Feature extraction methods regarding signals mainly include time domain, frequency domain, and time-frequency domain methods. To a certain extent, the time domain and frequency domain (e.g., Fourier transform) methods have limitations for complex and non-stationary signal processing [8]. In fact, there are many non-stationary, nonlinear, and dynamic signals in practical applications. Generally, the time-frequency domain methods (e.g., short-time Fourier transform (STFT), Wigner-Ville distribution (WVD), and wavelet-based methods) are more commonly used in signal analyzing compared with the time domain and frequency domain methods [9; 10; 11]. In STFT methodology, a given time signal is divided into equal length segments by windowing, to reveal the spectrum of each segment by applying a Fourier transform. However, in practice, it is usually difficult to decide the windowing function and the window length, for a better reflection of the signal spectrum [12]. The WVD and higher-order WVD methods can achieve better resolutions than STFT. However, for multicomponent signals, cross-term interference will occur [13]. The pseudo WVD, which was developed to suppress the cross-term, reduces the time-frequency resolution to a certain extent. The advantage of the wavelet-based methods is that they can process time series, which include non-stationary signals at many different frequencies [14]. The wavelet transform (WT) intercepts and analyzes the signal by adding a variable-scale sliding window on the signal [15]. Owing to the adjustable time-frequency window, the method meets the high-resolution requirements of the high-frequency signal. However, the resolution of WT in the time-frequency domain is related to the frequency--it gives a high time resolution in the high-frequency range and a high frequency resolution in the low-frequency range [16].