A Novel Unsupervised Graph Wavelet Autoencoder for Mechanical System Fault Detection

Reliable fault detection is an essential requirement for safe and efficient operation of complex mechanical systems in various industrial applications. Despite the abundance of existing approaches and the maturity of the fault detection research field, the interdependencies between condition monitoring data have often been overlooked. Recently, graph neural networks have been proposed as a solution for learning the interdependencies among data, and the graph autoencoder (GAE) architecture, similar to standard autoencoders, has gained widespread use in fault detection. However, both the GAE and the graph variational autoencoder (GVAE) have fixed receptive fields, limiting their ability to extract multiscale features and model performance. To overcome these limitations, we propose two graph neural network models: the graph wavelet autoencoder (GWAE), and the graph wavelet variational autoencoder (GWVAE). GWAE consists mainly of the spectral graph wavelet convolutional (SGWConv) encoder and a feature decoder, while GWVAE is the variational form of GWAE. The developed SGWConv is built upon the spectral graph wavelet transform which can realize multiscale feature extraction by decomposing the graph signal into one scaling function coefficient and several spectral graph wavelet coefficients. To achieve unsupervised mechanical system fault detection, we transform the collected system signals into PathGraph by considering the neighboring relationships of each data sample. Fault detection is then achieved by evaluating the reconstruction errors of normal and abnormal samples. We carried out experiments on two condition monitoring datasets collected from fuel control systems and one acoustic monitoring dataset from a valve. The results show that the proposed methods improve the performance by around 3%~4% compared to the comparison methods

Study, Implementation and Evaluation of Event Detection and Anomaly Identification Systems based on acoustic information

En la actualidad, el interés por la detección de eventos anómalos ha ido en aumento entre diferentes campos de investigación del estado del arte, como la visión por ordenador, el procesamiento de señales, la banca, etc. Las técnicas de Machine Learning (ML), y en concreto las técnicas de aprendizaje profundo, o Deep Learning (DL), han tenido un gran impacto en el desarrollo de las recientes aproximaciones,permitiendo grandes mejoras en cuanto a los índices de precisión de los sistemas propuestos. La visión por ordenador es el campo más avanzado en esta área. No obstante, existen sistemas en los que este problema se aborda a través de la información acústica proporcionada por un micrófono, o un conjunto de ellos, colocado en un entorno, debido a diferentes condicionantes: i) Privacidad del usuario; entornos en los que se debe monitorizar una situación y avisar si se encuentra alguna anomalía. Un ejemplo de este tipo de sistema es un sistema de detección de violencia doméstica desplegado en un hogar. ii) Mal funcionamiento de maquinaria; Componentes como el interior de un motor en donde es complejo instalar una cámara para comprobar el desgaste de las piezas o su correcto funcionamiento, abordar esta tarea con información acústica es una solución típica A partir de un estudio del estado actual del arte en la detección de eventos acústicos anómalos, se ha considerado utilizar un sistema existente para el desarrollo de este trabajo fin de grado. Los principales objetivos planteados han sido: reproducir los experimentos realizados por los desarrolladores del sistema elegido, consiguiendo así resultados similares; cambiar la base de datos utilizada para entrenar, validar y probar el sistema, con el fin de estudiar la adaptabilidad de la red a un nuevo tipo de datos; y modificar la red dada para estudiar el efecto que éstas tienen en el rendimiento del sistema. Además, se ha estudiado un segundo sistema. Dicho sistema, denominado SELDNet, es bien conocido en el estado del arte y se centra en la detección de eventos acústicos así como en la clasificación multiclase de los mismos. Aunque no se aproxima a la tarea de detección de eventos anómalos propuesta en este proyecto, es relevante su estudio ya que un primer paso para la detección de anomalías es la detección de los eventos acústicos.Nowadays, the interest in detecting anomalous events has been rising within different state-of-the-art research fields, such as computer vision, signal processing, banking and so on. Machine Learning techniques, and specifically Deep Learning techniques, have had a great impact on the recent approaches developed, allowing great improvements in terms of the accuracy rates of the proposed systems. Computer vision is the most advanced field in this area. Nevertheless, there are systems where this problem is addressed through the acoustic information provided by a microphone placed inside an environment, due to different constraints: i) User privacy; environments where a situation must be monitored and a warning given if an anomaly is found. An example of this kind of system is a domestic violence detection system deployed in a house. ii) Machinery malfunction; Components such as engines where it is complex to set up a camera inside to check the wear of the pieces or their correct operation, approaching this task with acoustic information is a typical solution. Based on a study of the current state of the art in the detection of anomalous acoustic events, it has been considered to use an existing system for the development of this degree final project. The main objectives set have been: to reproduce the experiments carried out by the chosen system developers, thus achieving similar results; to change the database used to train, validate and test the system, in order to study the adaptability of the network to a new type of data; and to modify the given network to study the effect that these have on the performance of the system. In addition, a second system has been studied. Said system, named as SELDNet, is well-known in the state of the art and focuses on the detection of acoustic events as well as on the multi-class classification of them. Although it does not approach the anomalous event detection task proposed in this project, it is relevant to study it since a first step for anomaly detection is the detection of the acoustic eventsGrado en Ingeniería en Tecnologías de Telecomunicació