Abstract
Early detection of fault events through electromechanical systems operation is one of the most attractive and critical data challenges in modern industry. Although these electromechanical systems tend to experiment with typical faults, a common event is that unexpected and unknown faults can be presented during operation. However, current models for automatic detection can learn new faults at the cost of forgetting concepts previously learned. This article presents a multiclass incremental learning (MCIL) framework based on 1D convolutional neural network (CNN) for fault detection in induction motors. The presented framework tackles the forgetting problem by storing a representative exemplar set from past data (known faults) in memory. Then, the 1D CNN is fine‐tuned over the selected exemplar set and data from new faults. Test samples are classified using nearest centroid classifier (NCC) in the feature space from 1D CNN. The proposed framework was evaluated and validated over two public datasets for fault detection in induction motors (IMs): asynchronous motor common fault (AMCF) and Case Western Reserve University (CWRU). Experimental results reveal the proposed framework as an effective solution to incorporate and detect new induction motor faults to already known, with a high accuracy performance across different incremental phases.
Highlights
induction motors (IMs) support most of the production process in the modern industry’s daily life due to their straightforward construction, reliability, and relatively low cost
Conclusions is study presents a multiclass incremental learning (MCIL) framework based on fine-tuning with a memory of exemplars and the nearest centroid classifier (NCC) over an 1D convolutional neural network (CNN), to incorporate new motor faults from vibration signals to already known
1D CNN is fine-tuned over samples from new faults and exemplars from known faults, whereas NCC is used during testing phase to classify samples from past and new faults. e proposed framework was evaluated over two datasets for motor fault diagnosis: asynchronous motor common fault (AMCF) and Case Western Reserve University (CWRU)
Summary
IMs support most of the production process in the modern industry’s daily life due to their straightforward construction, reliability, and relatively low cost. Ese operative conditions raise unexpected faults that can show up at any time, causing lower productivity and economic losses. Motor fault analysis methods split into signal processing and artificial intelligence approaches [1]. Artificial intelligencebased methods have been integrated to provide automatic fault detection using a data-driven approach. Ese methods base their performance on extracted features from raw signals to be used as inputs. Deep learning (DL) architectures, such as autoencoders (AE) [6], convolutional neural network (CNN) [5, 7, 8], and capsule networks (CapsNet) [1], have been used in fault diagnosis due to their potential applicability for the automatic feature extraction, reported in several cases new state-of-the-art results. Some authors [9,10,11,12] have shown some promising advances to eliminate the
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