# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 1 Introduction
Therefore, in this paper we report the investigation that was carried out in utilising SK based BSS method for damage detection in an asset monitoring application. [PERSON] and [PERSON] reported the theory of Spectral Kurtosis and applied this technique to detect rolling element bearing defects using a statistical thresholding approach [11]. [PERSON] and [PERSON] introduced the adaptive spectral kurtosis (ASK) method for the characterisation of rolling element bearing faults [12]. To obtain an optimal filter, [PERSON] and [PERSON] [13] proposed a technique based on the adaptive thresholding procedure applied to the SK estimate. As an alternative to the STFT, [PERSON] et al. [14] estimated SK using the variable resolution wavelet transform and applied wavelet spectral kurtosis for the diagnosis of gearbox defects. [PERSON] et al. [15] introduced the wavelet adaptive SK filtering technique for the diagnosis of gearbox tooth faults. However, few gaps listed below remain unsolved.List of unsolved problems or gaps in literature Numbered lists can be added as follows:  
1. Spikes in signals increase in SK significantly * Spikes in rolling element bearing vibration data are quite frequent. 2. Scalability of the SK data analysis pipeline * Previously reported approaches are applicable to few sets of datasets and cannot be generalised to other machinery data sets. 3. Higher noise in signals can cause errors in SK * The low SNR in acceleration signals causes false alarms in the signals and feature engineering failures. The use of the spike subtraction, stationary wavelet transforms, and automated change detection were not addressed by the previous authors. The spike subtraction algorithm restores each data point of the vector by its median estimated over a fixed window length. Accordingly, median filtering ensures all the outliers are eliminated. By implementingthe spike subtraction false alarms are significantly minimised which provides a reliable feature engineering tool. Each spike which is not correlated to the defect will cause the SK to produce a peak which will add noise to the SK spectrum. The wavelet transforms-based decomposition proposed in the later section of this paper will ensure the signal to noise ratio is high and increases the probability of correct diagnosis. Combination of these algorithms make the SK based approach more reliable and easier to adapt in an industrial setting. These algorithms improve the scalability of the proposed SK based defect detection pipeline. In addition, the change detection algorithm proposed provides a fool proof method for data visualisation and enables early diagnosis of bearing defects. This pipeline can be generalised and is adaptable to other rotating machinery such as gearboxes and structures with bolt looseness [16]. In general, bearing defects are localised to either the outer race, inner race or the cage of the support. When a bearing strikes a local defect (e.g., pitting due to wear), impulse excitation is observed. The impulse excitation (narrowband excitation) tends to excite the resonance of the bearing structure, which generates an impulse response. The technique that was was utilised is the blind source separation method involving Spectral Kurtosis based Weiner filtering. The results presented demonstrate an early bearing fault detection method developed using the vibration data. The raw vibration data is a combination of excitation from multiple individual vibration sources, the fault signature is masked with a weak signature. This paper consists of four sections. The detail of experiments and data acquisition is presented in Section 2. The proposed methodology is described in Section 3 explains the pre-processing algorithms and the SK-based blind feature extraction algorithm along with the performance. The conclusion and future work is presented in Section 4.