# Research on Fault Recognition of Roadheader Based on Multi-Sensor and Multi-Layer Local Projection
###### Abstract
The main principle of manifold learning for data processing is to consider high-dimensional signals as a manifold formed by the action of a few independent vectors in the monitoring space [15]. Manifold learning aims to identify these vectors acting on the monitoring space from the high-dimensional space, thereby discovering low-dimensional characteristic parameters of the signals. When mapping from high-dimensional to low-dimensional spaces, manifold learning can preserve local features of the high-dimensional data, ensuring retention and enhancement of the intrinsic data characteristics. Based on this, many scholars have applied manifold learning to equipment fault diagnosis. [PERSON] proposed an unsupervised bearing fault diagnosis method based on deep clustering. In this method, an autoencoder is initially applied to the signal spectrum to learn the initial representation. Experiments conducted on the bearing datasets show that the proposed method can find the optimal clusterable manifold [16]. [PERSON] proposed an improved manifold learning method, which can improve the effect of dimensionality reduction and increase the accuracy of bearing fault diagnosis in mechanical systems [17]. [PERSON] introduced a technique that utilizes kernel-robust manifold non-negative matrix factorization and feature fusion for the purpose of differential fault identification. This approach involved performing time-frequency analysis on vibration signals from diesel engines, extracting features through kernel-robust manifold non-negative matrix factorization, and employing support vector machines for fault diagnosis [18]. [PERSON] used LLE manifold learning to perform dimensionality reduction on the operational parameters of TBM construction, predicted further construction parameters and equipment operating states, and verified the reliability and validity of the prediction results through experiments [19]. [PERSON] applied locally linear embedding (LLE) to reduce the dimensionality of vibration parameters throughout the entire fault process of a roadheader. Employing a modifiedk-means clustering analysis, calculated the Calinski-Harabasz (CH) clustering effectiveness index in a lower-dimensional space. This index served as a quantified evaluation metric for health status monitoring (HEI) and was instrumental in generating degradation curves for critical components of a roadheader [20]. Vibration signals from roadheaders inevitably vary with different faults during operation. Enhancing the differentiation of signal features through data analysis is crucial to addressing this issue effectively. Accurately identifying the type and severity of faults is essential for devising maintenance strategies for tunneling machines. Despite potential differences in vibration features under various fault conditions, these distinctions may not be readily apparent. Single-feature extraction runs the risk of missing effective features or distorting target parameters, hindering fault identification. Although variations in vibration characteristics exist under different fault conditions of the roadheader, these differences are relatively minor. Relying on single-feature extraction can result in the omission of critical features or distortion of key parameters, which hampers the fault diagnosis of essential components. Therefore, this study focuses on multi-layer feature extraction and comparative learning of original vibration data parameters. It also involves multiple iterations of target-feature parameter learning using multiple vibration sensors, which significantly enhances fault differentiation and improves fault identification accuracy.