An effective zero-shot learning approach for intelligent fault detection using 1D CNN

Abstract

Data-driven fault detection techniques have attracted extensive attention in engineering, industry and many other areas in recent years. In many real applications, the following situation often occurs: data for certain types of faults (unseen faults) are not available to train models that are used for fault detection. Such a scenario can occur when data collection becomes highly time-consuming or destructive. To address this challenging problem, a novel fault detection method using zero-shot learning (ZSL) is proposed in this paper, which contains three phases: feature extraction, label embedding, and feature embedding. The method first extracts features from raw signals by applying a one-dimensional convolutional neural network (1D CNN), then builds semantic descriptions (human-defined) as fault attributes shared between seen faults and unseen faults, and finally uses a bi-linear compatibility function to find the highest-ranking fault type. The proposed semantic space based zero-shot learning with 1D CNN is called SSB-ZSL-1DCNN. The cosine distance is used to measure the similarity between feature embeddings and fault attributes. An important characteristic of SSB-ZSL-1DCNN is that the model, trained using only samples of seen faults, can be used to detect unseen defects. To evaluate the proposed method, two case studies are designed based on two well-known benchmarks (the Tennessee-Eastman chemical control process and the rolling bearing experiments at the Case Western Reserve University, respectively). The results demonstrate that the proposed method shows remarkable performance in detecting unseen faults.

Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey

“…There are works involving applications of process monitoring using several network architectures, such as recurrent networks [351][352][353][354][355][356][357][358][359][360][361][362][363][364][365][366][367], convolutional networks [299,349,[368][369][370][371][372][373][374][375][376][377][378][379][380][381][382], autoencoders [282,365,379,, generative adversarial networks [406][407][408][409][410][411][412][413][414][415][416], Transformers [417]…”

Aircraft Engine Fault Diagnosis Model Based on 1DCNN-BiLSTM with CBAM

“…Zhang S et al proposed a novel fault detection method using Zero Shot Learning (ZSL). This method first extracts features from the original signal by applying 1DCNN, then establishes semantic descriptions as shared fault attributes between known and unseen faults, and finally uses bilinear compatibility functions to find the highest level of bearing fault type [13].…”

A Hypothesis testing approach to Zero-Fault-Shot learning for Damage Component Classification

“…The ZSL, in this case, is, using nominal data from machine A, we classify a fault, such as a bearing spall without any fault example, on both machine, A, and B (figure 1). For application in mechanical diagnostics (Zhang, Wei, 2022) highlights the limited availability of fault datasets and the cost and time required to collect such data. As with other ZSL approaches, (Zhang, Wei, 2022) proposes a data augmentation strategy and transfer learning (Zhang et al 2022).…”

“…For application in mechanical diagnostics (Zhang, Wei, 2022) highlights the limited availability of fault datasets and the cost and time required to collect such data. As with other ZSL approaches, (Zhang, Wei, 2022) proposes a data augmentation strategy and transfer learning (Zhang et al 2022). In transfer learning, the diagnostic models reuse the previously learned knowledge is applied to the new diagnosis task, so that accurate fault identification can also be achieved using a few faulty samples.…”