Unsupervised Fault Detection in Varying Operating Conditions

Abstract

Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be performed in a unsupervised manner. In addition, when the observation period, used for training, is kept short, to be able to monitor the system in its early life, the training data might not be representative of all the system normal operating conditions. In this paper, we propose five approaches to perform fault detection in such context. Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit. An incremental learning procedure tries to learn new operating conditions as they arise. Three other approaches take advantage of data from other similar units within a fleet. In two cases, units are directly compared to each other with similarity measures, and the data from similar units are combined in the training set. We propose, in the third case, a new deep-learning methodology to perform, first, a feature alignment of different units with an Unsupervised Feature Alignment Network (UFAN). Then, features of both units are combined in the training set of the fault detection neural network. The approaches are tested on a fleet comprising 112 units, observed over one year of data. All approaches proposed here are an improvement to the baseline, trained with two months of data only. As units in the fleet are found to be very dissimilar, the new architecture UFAN, that aligns units in the feature space, is outperforming others.

Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

“…This allows for unsupervised detection of deviations in machine behaviour but relies on domain knowledge to segment the time series. Further unsupervised approaches include the UFAN architecture introduced by [13], who used data from different machines to be able to monitor an entire fleet, which is achieved using a generative adversarial network for low-dimensionality feature generation in combination with a one-class extreme learning machine (ELM) classifier. Jove et al [14] implement an anomaly detection for industrial control loops in an unsupervised manner by mapping data to twodimensional space and then setting limits in this space.…”

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

“…At the same time, unsupervised ML, also known as "learning without a teacher", is a type of learning where patterns are to be discovered from unknown data [44,45]. In this case, there is only training data, and the aim is to group objects into clusters and/or reduce a large amount of the given data.…”

Domain Adaptive Transfer Learning for Fault Diagnosis

“…Similarly, [8] propose to align the distributions of intermediate layers between source and feature extractors by adversarial training. [18] consider the problem of fault detection within a fleet using unsupervised feature alignment. Recently, [9] uses MMDminimization to align the full source and target distributions for rotationary machines.…”