Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Exploiting generative adversarial networks as an oversampling method for fault diagnosis of an industrial robotic manipulator

Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved.FCT-through IDMEC, under LAETA, project UIDB/50022/2020.info:eu-repo/semantics/publishedVersio

Comparación de señales de vibración y corriente para la detección de la severidad de fallos en engranajes

Está investigación tiene como objetivo comparar las señales de vibración y corriente de un motor de inducción para la detección de la severidad de fallos en engranajes. Para realizar el estudio de simularon los fallos de picadura y rotura de diente en engranes rectos con un total de nueve niveles de severidad para cada fallo. Como resultado mediante la aplicación de la FFT a las señales adquiridas se identificó la existencia de bandas laterales a través de la frecuencia de línea de alimentación del motor tales como; frecuencias de rotación del eje de entrada, eje de salida y frecuencias de engranaje, donde la vibración pudo detectar fallos incipientes y mediante la señal de corriente fallos severos.Palabras clave: afcm, engranes, m

Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles

Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features by applying the Mean Decrease Accuracy method of Random Forest. Finally, we test the best indicators with a K-Nearest Neighbor classifier. For the data collection, a real bogie test bench has been used to simulate crack faults on the railway axles, and vibration signals from both the left and right sides of the axle were measured. The results not only show the performance of condition indicators in different domains, but also show that the fusion of condition indicators works well together to detect a crack fault in railway axles.Authors would like to thank the support provided by the Spanish Government, through the MAQ-STATUS DPI2015-69325-C2-1-R project, and Universidad Politécnica Salesiana through the research group GIDTEC

Data size increment for fault detection on rotating machinery

En los últimos años se ha incrementado el uso de técnicas de modelamiento basado en datos para el diagnóstico de fallos en maquinaria rotativa. Estas técnicas requieren de grandes cantidades de datos que no siempre se pueden obtener pues generan  altos costos y tiempo excesivo, que son difíciles de solventar desde el punto de vista económico y técnico.  El presente trabajo se enfoca en el pre-procesamiento de las señales de vibración y propone un método para incrementar el número de series temporales informativas de una máquina rotativa sin el incremento del tiempo y costos en la etapa de adquisición de las señales. Como resultado se ha obtenido una ampliación de 315 señales en la fase de adquisición de datos a 429000 luego de la aplicación del método; cantidad adecuada para la construcción de modelos basados en datos, incluso de deep learning para la detección de fallos en maquinaria rotativa. Palabras clave: Adquisición de datos, pre-procesamiento, rodamientos, señales.