| 1. |
- Amirhossein, Berenji, et al.
(författare)
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curr2vib : Modality Embedding Translation for Broken-Rotor Bar Detection
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature. - 9783031236334 ; , s. 423-437
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Konferensbidrag (refereegranskat)abstract
- Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Multi-Modal Translation is capable of transferring knowledge from a modality with higher level of applicability (more usefulness to solve an specific task) but lower level of accessibility (how easy and affordable it is to collect information from this modality) to another one with higher level of accessibility but lower level of applicability. Unlike the fusion of multiple modalities which requires all of the modalities to be available during the deployment stage, our proposed method depends only on the more accessible one; which results in the reduction of the costs regarding instrumentation equipment. The presented case study demonstrates that by the employment of the proposed method we are capable of replacing five acceleration sensors with three current sensors, while the classification accuracy is also increased by more than 1%.
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| 2. |
- Berenji, Amirhossein, et al.
(författare)
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An Analysis of Vibrations and Currents for Broken Rotor Bar Detection in Three-phase Induction Motors
- 2022
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Ingår i: Proceedings of the European Conference of the Prognostics and Health Management Society 2022. - State College, PA : PHM Society. - 9781936263363 ; , s. 43-48
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Konferensbidrag (refereegranskat)abstract
- Selecting the physical property capable of representing the health state of a machine is an important step in designing fault detection systems. In addition, variation of the loading condition is a challenge in deploying an industrial predictive maintenance solution. The robustness of the physical properties to variations in loading conditions is, therefore, an important consideration. In this paper, we focus specifically on squirrel cage induction motors and analyze the capabilities of three-phase current and five vibration signals acquired from different locations of the motor for the detection of Broken Rotor Bar generated in different loads. In particular, we examine the mentioned signals in relation to the performance of classifiers trained with them. Regarding the classifiers, we employ deep conventional classifiers and also propose a hybrid classifier that utilizes contrastive loss in order to mitigate the effect of different variations. The analysis shows that vibration signals are more robust under varying load conditions. Furthermore, the proposed hybrid classifier outperforms conventional classifiers and is able to achieve an accuracy of 90.96% when using current signals and 97.69% when using vibration signals.
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| 3. |
- Berenji, Amirhossein, 1995-, et al.
(författare)
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Data-Centric Perspective on Explainability Versus Performance Trade-Off
- 2023
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Ingår i: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 42-54
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Konferensbidrag (refereegranskat)abstract
- The performance versus interpretability trade-off has been well-established in the literature for many years in the context of machine learning models. This paper demonstrates its twin, namely the data-centric performance versus interpretability trade-off. In a case study of bearing fault diagnosis, we found that substituting the original acceleration signal with a demodulated version offers a higher level of interpretability, but it comes at the cost of significantly lower classification performance. We demonstrate these results on two different datasets and across four different machine learning algorithms. Our results suggest that “there is no free lunch,” i.e., the contradictory relationship between interpretability and performance should be considered earlier in the analysis process than it is typically done in the literature today; in other words, already in the preprocessing and feature extraction step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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| 4. |
- Berenji, Amirhossein, et al.
(författare)
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Fault identification with limited labeled data
- 2024
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Ingår i: Journal of Vibration and Control. - London : Sage Publications. - 1077-5463 .- 1741-2986. ; 30:7-8, s. 1502-1510
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Tidskriftsartikel (refereegranskat)abstract
- Intelligent fault diagnosis (IFD) based on deep learning methods has shown excellent performance, however, the fact that their implementation requires massive amount of data and lack of sufficient labeled data, limits their real-world application. In this paper, we propose a two-step technique to extract fault discriminative features using unlabeled and a limited number of labeled samples for classification. To this end, we first train an Autoencoder (AE) using unlabeled samples to extract a set of potentially useful features for classification purpose and consecutively, a Contrastive Learning-based post-training is applied to make use of limited available labeled samples to improve the feature set discriminability. Our Experiments—on SEU bearing dataset—show that unsupervised feature learning using AEs improves classification performance. In addition, we demonstrate the effectiveness of the employment of contrastive learning to perform the post-training process; this strategy outperforms Cross-Entropy based post-training in limited labeled information cases. © The Author(s) 2023.
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| 5. |
- Taghiyarrenani, Zahra, 1987-, et al.
(författare)
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Noise-robust representation for fault identification with limited data via data augmentation
- 2022
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Ingår i: Proceedings of the European Conference of the Prognostics and Health Management Society 2022. - State College, PA : Prognostics and Health Management Society. - 9781936263363 ; , s. 473-479
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Konferensbidrag (refereegranskat)abstract
- Noise will be unavoidably present in the data collected from physical environments, regardless of how sophisticated the measurement equipment is. Furthermore, collecting enough faulty data is a challenge since operating industrial machines in faulty modes not only has severe consequences to the machine health, but also may affect collateral machinery critically, from health state point of view. In this paper, we propose a method of denoising with limited data for the purpose of fault identification. In addition, our method is capable of removing multiple levels of noise simultaneously. For this purpose, inspired by unsupervised contrastive learning, we first augment the data with multiple levels of noise. Later, we construct a new feature representation using Contrastive Loss. The last step is building a classifier on top of the learned representation; this classifier can detect various faults in noisy environments. The experiments on the SOUTHEAST UNIVERSITY (SEU) dataset of bearings confirm that our method can simultaneously remove multiple noise levels.
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