A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario

Das, Monidipa; Pratama, Mahardhika; Tjahjowidodo, Tegoeh

doi:10.1145/3394486.3403328

Cited by 8 publications

(14 citation statements)

References 11 publications

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“…Only the studies presented in [55] and [69] proposed methods for detecting faults directly from real-time data and applied stream learning techniques.…”

Section: Rq5: Which Of Those Algorithms and Methods Are Used For Data...mentioning

confidence: 99%

“…Instance-based algorithms K-NN [39] supervised Latent Variable Models PCA [65] unsupervised GMM [47] unsupervised K-Means [54] unsupervised PLSR [64] supervised K-SVD [60] unsupervised K-MDTSC [62] unsupervised Artificial Neural Networks ANN [57] supervised BPNN [40] supervised CNN [78] supervised DNN [77] supervised LSTM [70] supervised MLP [56] supervised SSAE + BPNN [31] unsupervised + supervised SSAE + Softmax Classifier [81] unsupervised + supervised LSTM Autoencoder [73] supervised LSTM -GAN [79] supervised RNN [55] supervised Conditional Variational Autoencoder [66] unsupervised Rule-based models R4RE ("Rules 4 Rare Events" based on QARMA) [49] supervised XCSR [51] supervised consists in the principal components obtained from the application of DPCA, which do not represent any physical properties or measurements of the system. The study presented in [67] used an ensemble method as well due to its efficiency in terms of computation time and memory when handling large amounts of data.…”

Section: Decision Treesmentioning

confidence: 99%

“…Changes in the data distribution were also the focus of the method proposed in [55]. SERMON is a model capable of modelling the temporal dependency present in streaming Table 13 Advantages and limitations of the artificial neural networks employed for mechanical fault detection and fault prognosis…”

Section: Not Identifiedmentioning

confidence: 99%

“…Not identified [55] Proposed approach: online learning capability; autonomous structural evolution; capable of adapting to drifts in the input data; capable of learning under finitely/infinitely delayed label scenarios.…”

Section: Publicationmentioning

confidence: 99%

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Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n=12), decision tree methods (n=11), hybrid models (n=8), or latent variable models (n=6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

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“…Only the studies presented in [55] and [69] proposed methods for detecting faults directly from real-time data and applied stream learning techniques.…”

Section: Rq5: Which Of Those Algorithms and Methods Are Used For Data...mentioning

confidence: 99%

Section: Decision Treesmentioning

confidence: 99%

Section: Not Identifiedmentioning

confidence: 99%

Section: Publicationmentioning

confidence: 99%

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Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

2022

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“…These two approaches are nondeep-learning approach. The problem of labeling delay is addressed via the dynamic skip connection of recurrent neural network in [19]. In [20], the deep learning approach via the SLASH method is put forward.…”

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confidence: 99%

Unsupervised Continual Learning in Streaming Environments

Ashfahani¹,

Pratama²

2021

Preprint

Self Cite

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A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of the deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This paper presents an unsupervised approach of deep clustering network construction on the fly via simultaneous deep learning and clustering termed Autonomous Deep Clustering Network (ADCN). It combines the feature extraction layer and autonomous fully connected layer in which both network width and depth are self-evolved from data streams based on the bias-variance decomposition of reconstruction loss. The self-clustering mechanism is performed in the deep embedding space of every fully connected layer while the final output is inferred via the summation of cluster prediction score. Further, a latent-based regularization is incorporated to resolve the catastrophic forgetting issue. A rigorous numerical study has shown that ADCN produces better performance compared to its counterparts while offering fully autonomous construction of ADCN structure in streaming environments with the absence of any labeled samples for model updates. To support the reproducible research initiative, codes, supplementary material, and raw results of ADCN are made available in https://tinyurl.com/AutonomousDCN.

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A dual-model semi-supervised self-organizing fuzzy inference system for data stream classification

2023

Applied Soft Computing

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A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario

Cited by 8 publications

References 11 publications

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Unsupervised Continual Learning in Streaming Environments

A dual-model semi-supervised self-organizing fuzzy inference system for data stream classification

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