Deep dense and convolutional autoencoders for machine acoustic anomaly detection

Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) - Project n ∘ 039334; Funding Reference: POCI-01-0247-FEDER-039334

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

The growing usage of digital microphones has generated an increased interest in the topic of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application domains, including working machines and in-vehicle intelligence (the main target of this research project). This paper introduces three deep AutoEncoders (AE) for unsupervised AAD tasks, namely a Dense AE, a Convolutional Neural Network (CNN) AE and Long Short-Term Memory Autoencoder (LSTM) AE. To tune the deep learning architectures, development data were adopted from public domain audio datasets related with working machines. A large set of computational experiments was held, showing that the three proposed deep autoencoders, when combined with a melspectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Next, on a second experimental stage, aiming to address the final in-vehicle passenger safety goal, the three AEs were adapted to learn from in-vehicle normal audio, assuming three realistic scenarios that were generated by a synthetic audio mixture tool. In general, a high quality AAD discrimination was obtained: working machine data - 72% to 91%; and in-vehicle audio - 78% to 81%. In conjunction with an automotive company, an in-vehicle AAD intelligent system prototype was further developed, aiming to test a selected model (LSTM AE) during a pilot demonstration event that targeted the cough anomaly. Interesting results were obtained, with the AAD system presenting a high cough classification accuracy (e.g., 100% for front seat locations).This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) - Project no 039334; Funding Reference: POCI-01-0247-FEDER-039334

Condition monitoring based on anomalous sound detection via autoencoders

openCondition Monitoring is the process of monitoring a parameter of a particular machine, for the purpose of identifying developing anomalies. In this thesis, in cooperation with 221e S.r.l. during an internship, an autoencoder-based Condition Monitoring system is proposed, with the aim of detecting anomalies in machines using sound signals. Sound indicators in Condition Monitoring offer multiple advantages over more traditional metrics like temperature, vibration, or voltage. Anomalies can be detected before major malfunctions occur, the machine can be monitored without physical contact and a large set of different anomalies can be detected. The system was developed and evaluated on three real user scenarios provided by the company. Different conditions and settings, with customized data acquisitions and training of the model, were considered. In the end, an embedding of the monitoring solution into the microcontroller multi-sensor board STWIN is considered and validated on the device

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

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

Error Cause Analysis of Laboratory Results with the Help of AI

In the electronics laboratory, a large amount of different devices has to be tested, and the characterization of each of them generates a large amount of data. Classical root cause analysis is inherently inefficient because it requires manual inspection by experts, turning out to be costly and time consuming. Furthermore, automatic evaluations through sequences of conditions for the signals, ending up in hard-coded logical formulas, are still inappropriate due to still the necessity of experts, in order to design them specifically for each different device, and to the complexity of the data itself, which may lead to the infeasibility of such approach. For these reasons, in this thesis, first steps towards a machine learning (ML) approach are investigated, laying the foundation into the transition to a ML root cause analysis approach.In the electronics laboratory, a large amount of different devices has to be tested, and the characterization of each of them generates a large amount of data. Classical root cause analysis is inherently inefficient because it requires manual inspection by experts, turning out to be costly and time consuming. Furthermore, automatic evaluations through sequences of conditions for the signals, ending up in hard-coded logical formulas, are still inappropriate due to still the necessity of experts, in order to design them specifically for each different device, and to the complexity of the data itself, which may lead to the infeasibility of such approach. For these reasons, in this thesis, first steps towards a machine learning (ML) approach are investigated, laying the foundation into the transition to a ML root cause analysis approach

Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC

Two stage system for anomalous sound detection in industrial environments

Machine breakdowns and maintenance breaks cause costly downtime in factories and power plants. Recognizing a breaking machine before the actual breakdown can reduce the downtime and size of the damage. The existing condition monitoring systems are usually based on measuring the vibrations in the machines. In industrial environments the acoustic properties are relatively homogeneous during normal operation, and machine failures cause change in those properties. Therefore a change in acoustic conditions reflects an anomalous event that can be detected through analysis of audio signals at the scene. However, sometimes normal operation like talking or door slamming can cause a significant change in the acoustic conditions, and those should be ignored. This thesis presents a two-stage acoustic anomaly detection system. The motivation behind using two stages is to offer the operator a possibility to silence certain anomaly types. This makes it possible to ignore normal events that are anomalies from acoustic point of view but do not indicate a need for alarm