# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 1 Introduction  
Statistical process control has been extensively used for condition monitoring of industrial machinery. Vibration-based condition monitoring is considered a primary tool for performing necessary day-to-day inspections as this is a non-invasive procedure. Vibration data from rotating machinery has multiple frequency components. For rotating machinery which involves oil lubricant fluid interaction, the complexity of the frequency combinations is higher. Usually, abnormal patterns in the vibration data indicate an abnormal behaviour that might cause issues to either the process or the processing equipment. These abnormal patterns generate non-stationary components that are buried in time-series vibration signals and are difficult to extract [1]. Given this challenge, there is a need to develop efficient algorithms to identify abnormal vibration patterns and label the cause of the defect. Number of researchers have developed algorithms to automate the detection of an abnormal pattern from the normal raw vibration pattern. Automating the detection process is crucial in these situations as the source of the vibration patterns and the process through which they mix is unknown. In multiple situations, it is very useful to identify the individual sources of each frequency components, either to eliminate obvious frequency components which do not represent the defect or to verify the output performance with known inputs. Many diverse algorithms are developed to tackle the Blind Source Separation (BSS) problem, and these algorithms are developed based on higher-order statistical features as they are suitable to recognise Non-Gaussian signals from Gaussian ones. Other techniques such as Entropy Minimisation (EM) and Minimal Mutual Information (MMI) are suitable tosolve a BSS problem [2]. BSS can also be used for image separation for pulse thermography detection in detecting debonding defects of solid propellant rocket motor cladding layer [3]. Independent Component Analysis (ISA) is another popular choice to tackle the BSS problem, and it is used to extract features such as modal parameters and singular values for machine fault [4; 5; 6]. In addition, [PERSON] et al. [7] have implemented a combination of ICA and Support Vector Machine (SVM) technique for concurrent control charts pattern recognition for industrial process monitoring. Deep learning approach such as Neural Network (NN) is often used to perform feature extraction algorithms due to their self-learning process to realise different features. This is also proven to be a promising technique to solve the BSS problem. [PERSON] et al. [8] have proposed a NN with bias term and maximum likelihood estimation criterion technique to improve the convergence speed of source separation performance that most BSS algorithms suffer from. It is validated with actual data recorded in a real ship's engine room to separate the key component frequencies and the result proves its effectiveness in practical engineering applications. However, the NN approach has limitations that require a large amount of data to achieve good accuracy and the transferability is limited to a specific application. The amount of time to train a model is also demanding. To summarise the fundamental approach of BSS, [PERSON] et al. [9] have categorised this into four types as listed below:  
* Non-stationary, Time-varying variances;
* Time-Frequency, Spectral/Spatial diversities;
* Temporal Structure, Non-Whiteness;
* Mutual Independence, Non-Gaussianity and ICA  
Spectral Kurtosis (SK) is a statistical spectral analysis technique that can be used to characterise non-stationary time signals. This is based on spectral analysis of the time series analysis Kurtosis and is an enhancement to the classic power spectrum density (PSD). It is capable of detecting non-stationary components in noisy transient signals by finding a closed-form relationship in regard to signal-to-noise ratio [10].