Domain Adaptive Transfer Learning for Fault Diagnosis

Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although data-driven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.Comment: Presented at 2019 Prognostics and System Health Management Conference (PHM 2019) in Paris, Franc

Assessment of maintenance strategies for railway vehicles using Petri-Nets

The density of railway traffic has been steadily increasing over past years and decades. The developments have implicated a growing need for efficient operation and maintenance of railway rolling stock systems. Also the increased operation of articulated trains has induced new challenges on maintenance organization and planning. Selecting optimal maintenance strategies for each component does not only influence the availability of the railway vehicles but also the operational performance and the profitability of the operator. Suitable tools to analyse, compare and optimize different maintenance strategies are therefore required. Petri nets are such a mathematical tool that and have been applied for maintenance modeling and simulations of different applications. Several types of Petri nets with different properties have been introduced. One of the recently proposed extensions of Petri nets are the Abridged Petri Nets (APN) which fulfill the specific requirements of railway rolling stock maintenance. In this paper, we propose the application of APN in combination with the Monte-Carlo simulation for railway rolling stock maintenance evaluation. In a first step, the applicability of the APN approach was demonstrated on a theoretical case study comprising a condition based maintenance strategy for a system. In a second case study, several real application case studies were modeled and compared based on the processes and real application field data of three railway vehicle components. The tool can be further extended by pre-defining selected strategies that be easily implemented within an overall decision support system

Dynamic Graph Attention for Anomaly Detection in Heterogeneous Sensor Networks

In the era of digital transformation, systems monitored by the Industrial Internet of Things (IIoTs) generate large amounts of Multivariate Time Series (MTS) data through heterogeneous sensor networks. While this data facilitates condition monitoring and anomaly detection, the increasing complexity and interdependencies within the sensor network pose significant challenges for anomaly detection. Despite progress in this field, much of the focus has been on point anomalies and contextual anomalies, with lesser attention paid to collective anomalies. A less addressed but common variant of collective anomalies is when the abnormal collective behavior is caused by shifts in interrelationships within the system. This can be due to abnormal environmental conditions like overheating, improper operational settings resulting from cyber-physical attacks, or system-level faults. To address these challenges, this paper proposes DyGATAD (Dynamic Graph Attention for Anomaly Detection), a graph-based anomaly detection framework that leverages the attention mechanism to construct a continuous graph representation of multivariate time series by inferring dynamic edges between time series. DyGATAD incorporates an operating condition-aware reconstruction combined with a topology-based anomaly score, thereby enhancing the detection ability of relationship shifts. We evaluate the performance of DyGATAD using both a synthetic dataset with controlled varying fault severity levels and an industrial-scale multiphase flow facility benchmark featuring various fault types with different detection difficulties. Our proposed approach demonstrated superior performance in collective anomaly detection for sensor networks, showing particular strength in early-stage fault detection, even in the case of faults with minimal severity.Comment: 15 pages, 7 figure

Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.Comment: Under review / added arXiv identifie

Quantifying the reliability of fault classifiers

International audienceFault diagnostics problems can be formulated as classification tasks. Due to limited data and to uncertainty, classification algorithms are not perfectly accurate in practical applications. Maintenance decisions based on erroneous fault classifications result in inefficient resource allocations and/or operational disturbances. Thus, knowing the accuracy of classifiers is important to give confidence in the maintenance decisions. The average accuracy of a classifier on a test set of data patterns is often used as a measure of confidence in the performance of a specific classifier. However, the performance of a classifier can vary in different regions of the input data space. Several techniques have been proposed to quantify the reliability of a classifier at the level of individual classifications. Many of the proposed techniques are only applicable to specific classifiers, such as ensemble techniques and support vector machines. In this paper, we propose a meta approach based on the typicalness framework (Kolmogorov's concept of randomness), which is independent of the applied classifier. We apply the approach to a case of fault diagnosis in railway turnout systems and compare the results obtained with both extreme learning machines and echo state networks

Deep feature learning network for fault detection and isolation

Prognostics and Health Management (PHM) approaches typically involve several signal processing and feature engineering steps. The state of the art on feature engineering, comprising feature extraction and feature dimensionality reduction, often only provides specific solutions for specific problems, but rarely supports transferability or generalization: it often requires expert knowledge and extensive intervention. In this paper, we propose a new integrated feature learning approach for jointly achieving fault detection and fault isolation in high-dimensional condition monitoring data. The proposed approach, based on Hierarchical Extreme Learning Machines (HELM) demonstrates a good ability to detect and isolate faults in large datasets comprising signals of different natures, non-informative signals, non-linear relationships and noise. The method includes stacked auto-encoders that are able to learn the underlying high-level features, and a one-class classifier to combine the learned features in an indicator that represents the deviation from the normal system behavior. Once a deviation is identified, features are used to isolate the most deviating signal components. Two case studies highlight the benefits of the approach: First, a synthetic dataset with the typical characteristics of condition monitoring data and different types of faults is applied to evaluate the performance with objective metrics. Second, the approach is tested on data stemming from a power plant generator interturn failure. In both cases, the results are compared to other commonly applied approaches for fault isolation

Maximal information-based nonparametric exploration for condition monitoring data

The system condition of valuable assets such as power plants is often monitored with thousands of sensors. A full evaluation of all sensors is normally not done. Most of the important failures are captured by established algorithms that use a selection of parameters and compare this to defined limits or references. Due to the availability of massive amounts of data and many different feature extraction techniques, the application of feature learning within fault detection and subsequent prognostics have been increasing. They provide powerful results. However, in many cases, they are not able to isolate the signal or set of signals that caused a change in the system condition. Therefore, approaches are required to isolate the signals with a change in their behavior after a fault is detected and to provide this information to diagnostics and maintenance engineers to further evaluate the system state. In this paper, we propose the application of Maximal Information-based Nonparametric Exploration (MINE) statistics for fault isolation and detection in condition monitoring data. The MINE statistics provide normalized scores for the strength of the relationship, the departure from monotonicity, the closeness to being a function and the complexity. These characteristics make the MINE statistics a good tool for monitoring the pair-wise relationships in the condition monitoring signals and detect changes in the relationship over time. The application of MINE statistics in the context of condition monitoring is demonstrated on an artificial case study. The focus of the case study is particularly on two of the MINE indicators: the Maximal information coefficient (MIC) and the Maximum Asymmetry Score (MAS). MINE statistics prove to be particularly useful when the change of system condition is reflected in the relationship between two signals, which is usually difficult to be captured by other metrics