# Research on Fault Recognition of Roadheader Based on Multi-Sensor and Multi-Layer Local Projection
###### Abstract  
The working environment at coal mining faces is harsh, leading to high failure rates and significant maintenance issues with roadheaders. This study explores multi-layer dimensionality reduction of vibration signal features in complex environments to enhance the differentiation of different operational states of a roadheader, thereby achieving fault recognition of key components. Concurrently, reducing dimensionality in manifold spaces positively influences operational state differentiation. Therefore, this paper integrates manifold learning to conduct multi-sensor and multi-layer data mining to enhance the differential phenotypes between faults of key components of the roadheader. Initially, we constructed multiple status-reference sample sets for each sensor individually, forming multiple manifolds at different spatial points, and utilizing locality-preserving projections (LPP) to extract low-dimensional manifold features. Further fusion of low-dimensional features from multiple sensors was used to elevate samples, constructing an enhanced spatial pseudo-manifold. Finally, we used LPP to re-reduce the enhanced sensitive feature set from multiple vibration sensors, establishing a dual-layer sensitive feature enhancement learning model. Conducting fault recognition analysis on experimental vibration signals, using k-nearest neighbors (KNN) to classify the enhanced feature set, we achieved a recognition success rate of 98.75% for samples, proving the method's feasibility in fault recognition under complex loads. roadheader; vibration signal; fault recognition; locality-preserving projections 2 In recent years, industry scholars have conducted extensive research on the analysis of loading and fault diagnosis of roadheaders using vibration signals. [PERSON] and [PERSON] introduced a recognition method that utilizes a combination of multilayer neural networks and evidence theory. By integrating vibration data, current data, and hydraulic cylinder data, this approach enables real-time extraction of the dynamic load on the rock roadheader, capitalizing on the strengths of artificial neural networks (ANN) and evidence theory [8; 9]. Based on multi-body dynamics theory, [PERSON] established a multi-rigid body dynamics model of the roadheader, determined the cutting load of the roadheader, and used it to conduct a simulation model to study the effects of hydraulic system damping, a rigid body, and mass changes of key structures on vibration, obtaining the vibration characteristics of the cutting head, cantilever, and machine body [10]. [PERSON] and [PERSON] used wavelet-based processing methods to denoise the measured vibration signals in order to obtain better tunnel-boring machine vibration signals and implemented real-time processing of the vibration signals from the roadheader [11; 12]. [PERSON] studied six different machine learning algorithms and combined various machine learning algorithms through ensemble techniques to analyze the vibration signals of a tunnel boring machine. The six algorithms are random forest (RF), Gaussian process, linear regression, logistic regression, multilayer perceptron (MLP), and principal component analysis (PCA). The results showed that the results of the MLP and RF algorithms were better than those of the other algorithms, and the best solution was the bagging technique of RF and principal component analysis (PCA) [13]. [PERSON] used four machine learning tools (genetic algorithm-based backpropagation neural network, genetic algorithm-based naive Bayes optimization, particle swarm optimization-based support vector machine optimization, and dynamic inspection-based support vector machine) to analyze the vibration data of a tunnel boring machine and achieved early fault detection and identification [14]. Manifold learning is widely applied in signal processing, pattern recognition, and data analysis.