Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Abstract

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery

“…The goal of novelty detection is to recognize novel conditions that can evolve during machine running [11]. It is a Machine Learning problem that aims to identify new concepts in unlabeled data [31]. Novelty detection is often faced as a one-class classification problem, in which classification models are trained only on the nominal class [32].…”

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

“…Li et al [37] proposed a fault detection method based on the support vector machine (SVM) observer, realizing the novelty detection of astronomical telescope drive systems. Górski et al [38] designed a detection system for parallel gearboxes, comparing various rotating machinery fault novelty detection methods and validating their effectiveness on the gearbox dataset through experiments. The aforementioned methods have important theoretical implications for our research.…”

Review of Launch Vehicle Engine PHM Technology and Analysis Methods Research

“…On the other hand, in the design of complex PHM systems for intelligent launch vehicle engines, fault diagnosis algorithms are often determined based on system design objectives, such as time sensitivity, detection accuracy, coverage of faults, coverage of operating conditions, etc. If there are too many system design objectives, a single algorithm is often difficult to meet the requirements, and it is also necessary to integrate multiple methods to complete the diagnosis and prediction of faults in parallel [48][49][50]. For instance, Brotherton T has developed techniques that couple neural nets with automated rule extractors to form systems that have good statistical performance, easy system explanation and validation, potential new data insights and new rule discovery, novelty detection, and real-time performance [51].…”