Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives

Frede Blaabjerg; Huai Wang; Subham Sahoo

REVIEW 4 major objections 5 minor 19 references

Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that Bayesian neural networks can quantify prediction uncertainty in gear-fault diagnosis, so a sharp rise in uncertainty flags an unseen fault or noisy data and triggers human intervention.

desk verdict A plausible but under-validated application of Bayes-by-backprop to gear fault diagnosis; the qualitative uncertainty behavior is textbook, but no metrics, code, or data support the 'trustworthy diagnosis' gate. read the letter →

arxiv 2412.01272 v2 pith:ESFLO5HE submitted 2024-12-02 eess.SY cs.AIcs.SY

Subham Sahoo , Huai Wang , Frede Blaabjerg This is my paper · ORCID

classification eess.SYcs.AIcs.SY

keywords BayesianneuralnetworksuncertaintyquantificationfaultdiagnosisgearfaultsmotordrivesaleatoricepistemicBayesbybackprop

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that Bayesian neural networks (BNNs) can make fault diagnosis in motor drives trustworthy by quantifying the uncertainty in each prediction. The central claim is that a BNN trained on a set of known gear faults will show a sharp rise in predictive uncertainty when it meets a fault class it has never seen, and a comparable rise in aleatoric uncertainty when the input signals become noisy. The paper demonstrates this on an experimental gearbox simulator with three training fault classes, then progressively expands the training set with new fault classes and shows that diagnostic uncertainty drops once a class is learned. If the claim holds, operators get a practical signal for when an AI diagnosis can be trusted and when it should be handed to a human.

What carries the argument

The central mechanism is Bayes-by-backprop (BBB) variational inference, in which each network weight is represented by a variational distribution $q_\theta(\omega)$ instead of a point estimate. The parameters $\theta$ are learned by reparameterizing weights as $w = t(\theta, \epsilon)$ with a random variable $\epsilon$, which allows gradients of the variational objective to be computed by backpropagation through the sampled weights (Eqs. 7-8). The trained BNN then outputs a predictive distribution, and the total predictive uncertainty is decomposed into aleatoric uncertainty (data noise) and epistemic uncertainty (model knowledge), per Eq. (3). A deliberately small architecture — two hidden layers of 128 and 64 neurons, one 3-class output layer, stochastic gradient descent at learning rate 0.001 — carries the classification, and the uncertainty decomposition is what the authors use to flag unseen faults and noise.

What would settle it

Acquire a held-out set of healthy no-fault samples with the same sensor noise level used in training, and feed them to the trained BNN: if the predictive uncertainty on these in-distribution samples is as high as the uncertainty produced by the unseen eccentricity fault, then the 'alarming uncertainty' signal cannot distinguish seen from unseen conditions. Separately, report the BNN's classification accuracy and expected calibration error on the seen test set; if accuracy is at chance level or the uncertainty is miscalibrated, the claim that rising uncertainty flags untrustworthy diagnosis collapses.

Watch

Extended reading notes

Core claim

The paper's discovery is that Bayesian neural networks, which place probability distributions over network weights, produce a predictive uncertainty that behaves as a reliable alarm for out-of-distribution conditions. Concretely, when the BNN trained on 'No Fault,' 'Missing Tooth,' and 'Chipped Tooth' classes was tested on an unseen 'Eccentricity' fault, the confidence of its class predictions collapsed, with the upper quantile of the predictions dropping to 0.94 under noise, and a 'big discrepancy' appearing for the unseen fault (Fig. 6). When the input signals were corrupted with high noise (SNR below -25 dB), the aleatoric uncertainty rose significantly, dominating the total uncertainty (Fig. 7). The paper also shows an incremental learning property: as new fault labels are added to the training set, the diagnostic uncertainty drops and the newly seen fault is correctly classified (Fig. 8). The proposed use of these findings is a two-stage decision loop: a trustworthy diagnosis when uncertainty is low, and a human-intervention trigger when uncertainty is high (Fig. 9).

Load-bearing premise

The load-bearing premise is that the three electrical signals (DC-link voltage and the two stator currents) actually discriminate the gear faults, since the paper reports overlapping torque and DC-voltage profiles for fault vs. no-fault and gives no numeric accuracy on the seen test set.

Editorial extensions

If this is right

A BNN can serve as a gatekeeper: when predictive uncertainty exceeds a threshold, the diagnosis is flagged untrustworthy and referred to a human, rather than being accepted on the strength of a single point-estimate probability.
Adding new fault classes to the training set reduces diagnostic uncertainty and corrects prior misclassifications, which means the same model can be updated incrementally as new fault data becomes available.
Because noise raises aleatoric uncertainty while missing data raises epistemic uncertainty, the decomposition gives operators a diagnosis of why the model is unsure, not just that it is unsure.
The approach extends beyond gears to other power-electronics components where fault data is scarce and where overconfident deep-learning outputs are a known risk.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper reports no numeric classification accuracy on the seen test set; a natural next measurement is to compare BNN accuracy with the CNN and ResNet baselines on the same folds, to see whether the uncertainty benefit costs accuracy.
The 'alarming value' of uncertainty is described qualitatively; converting the flag into a calibrated threshold (e.g., via reliability diagrams) would be the deployment step that turns Fig. 9 into a working alarm.
Since Section II-B shows the torque and DC-voltage profiles overlap between surface-fault and no-fault cases, an informative extension is to train the BNN on each electrical signal separately (Vdc, Isx, Isy) to isolate which feature actually carries the discriminatory information that the uncertainty signal depends on.
The variational posterior's accuracy is assumed rather than checked; comparing BBB uncertainty with a gold-standard MCMC posterior on a small subset would test whether the reported epistemic uncertainty is trustworthy.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

Here is my take on arXiv:2412.01272. The paper applies Bayes-by-backprop to gear fault diagnosis on a physical testbed and shows qualitatively that predictive uncertainty rises for an unseen eccentricity fault and for increased noise. That is real, and the comparison against CNN and ResNet is sensible. The incremental retraining sequence (BNN-1 through BNN-4) is a nice visual demonstration that adding a fault class to training shrinks uncertainty on that class. The authors also deserve credit for acknowledging in Section II-B that the torque and DC-link voltage profiles show no visible statistical difference between surface fault and no-fault; that is a genuine limitation, and admitting it is more than many engineering papers do.

The soft spots are significant. There is no numeric accuracy, confusion matrix, or calibration metric anywhere. Without seen-domain accuracy, the 'alarming' uncertainty on the eccentricity fault cannot be separated from a generic distribution shift. The paper uses Vdc, Isx, Isy as inputs, yet Section II-B undercuts the discriminative power of those features, and no filtering or feature-engineering step is shown. The uncertainty decomposition in Section III-B is under-specified: PU = EU + AU is stated, but no estimators are given for the two components, so Fig. 7's aleatoric rise is not reproducible. Section IV has a duplicated paragraph in the Fig. 8 descriptions, which suggests a rushed draft. The incremental results are, to a large degree, guaranteed by construction: training on a new fault class lowers uncertainty on that class. That is not a surprise, though it is a useful illustration.

On the central claim: the qualitative behavior of the BNN follows from standard Bayesian theory, so the paper is not wrong. It is under-validated. There is no code, no data, no comparison to MC Dropout or deep ensembles, and no diagnostic checks on the variational posterior. As an engineering application, this is a plausible first step, but the trustworthy-diagnosis gate in Fig. 9 is not supported by the evidence as presented.

Who is this for? Practitioners in power electronics who want a concrete example of Bayesian uncertainty applied to fault diagnosis will find the testbed and the visualizations useful. Researchers familiar with Bayesian deep learning will see nothing new beyond the application.

I would send it to peer review only under the condition that the authors release code and data and add quantitative metrics: classification accuracy, calibration error, and AUROC for out-of-distribution detection. If that happens, it could be a reasonable conference paper. As is, it is a conditional reject that deserves a serious referee's time because the problem is important and the physical testbed is not something to brush aside.

Best,

Referee Report

4 major / 5 minor

Summary. The manuscript proposes an uncertainty-aware Bayesian neural network (BNN) using Bayes-by-backprop for gear fault diagnosis in a laboratory motor-drive setup. The BNN is trained on signals Vdc, Isx, and Isy for three seen fault classes (No Fault, Missing Tooth, Chipped Tooth) and is then evaluated on an unseen eccentricity fault and on noisy test data. The central claims are that BNN predictive uncertainty rises to an 'alarming value' for the unseen fault (Section IV, Fig. 6), that increased noise produces a significant rise in aleatoric uncertainty (Section IV, Fig. 7), and that incremental retraining with new fault classes reduces diagnostic uncertainty (Section IV, Fig. 8), leading to a trustworthy-diagnosis decision gate (Fig. 9). The paper also provides background on aleatoric versus epistemic uncertainty and on variational inference.

Significance. If the claims were fully supported, the work would make a useful contribution to trustworthy AI for power-electronics fault diagnosis: uncertainty estimates could complement point-estimate accuracy and trigger human intervention for out-of-distribution conditions. The manuscript has strengths worth acknowledging: it uses a real experimental gearbox simulator, compares against CNN and ResNet baselines, and demonstrates an incremental learning workflow. However, the empirical support is mostly qualitative. No classification accuracy, confusion matrix, calibration curve, or repeated-run variability is reported, and the uncertainty decomposition is not defined by equations. The central demonstration is therefore an illustration of known Bayesian properties rather than a validated diagnostic tool.

major comments (4)

[Section II-B and Section IV (Fig. 6)] The input features may not discriminate the fault classes. Section II-B explicitly states that for the same loading profile, torque and DC-link voltage show no visible statistical difference between a surface fault and no fault, and attributes this to missing filters or poor understanding of parameter interactions. Yet the BNN is fed Vdc, Isx, and Isy without any feature-engineering or filtering step being shown, and the paper reports no classification accuracy, confusion matrix, or calibration for the seen-domain classes. Without evidence that the BNN separates the three seen classes from these features, the elevated uncertainty on the unseen eccentricity fault cannot be distinguished from a generic distribution shift, so the central trustworthy-diagnosis claim is unsupported.
[Section III-B, Eq. (3), and Fig. 7] The uncertainty decomposition is not reproducible as stated. Equation (3) defines predictive uncertainty as the sum of epistemic and aleatoric uncertainty, but no estimators for EU and AU are given in terms of the BNN's predictive distribution or Monte Carlo samples. The claim in Fig. 7 that aleatoric uncertainty rises significantly when SNR is below -25 dB therefore cannot be checked. The authors should provide explicit formulas (for example, based on predictive entropy or mutual information) and state the number of Monte Carlo samples used.
[Section IV, Fig. 6] The paper's own description of Fig. 6 undermines the specificity of the uncertainty alarm. The text says the benchmarking models are 'fairly accurate' for seen faults but also that the BNN's confidence level is 'low for all the three seen fault conditions.' If the BNN gives low confidence even for seen faults, then high uncertainty is not a specific indicator of an unseen fault. A quantitative comparison of predictive confidence and accuracy on seen versus unseen samples, together with a threshold rule for the proposed alarm in Fig. 9, is needed to support the central claim.
[Section IV, Fig. 8] The incremental training results are asserted without quantitative support. The text states that updating the BNN with a new fault class 'automatically minimizes the diagnostic uncertainty and accurately identifies' the fault, but no accuracy or uncertainty numbers are provided for BNN-2, BNN-3, or BNN-4. Because adding the fault class to the training set trivially exposes the model to that class's data, the result is expected; a comparison with retraining from scratch, or at least before/after uncertainty values for the newly added fault, is needed to substantiate the claim.

minor comments (5)

[Section III, first paragraph] Typo: 'high generaltion' should be 'high generalization.'
[Section II-B and Fig. 4/Fig. 5] The text says 'see Fig. 5' for the surface-fault versus no-fault comparison, but the relevant figure appears to be Fig. 4; the figure numbering and cross-references should be checked.
[Section IV, Fig. 8 captions/description] The paragraph starting 'We then incorporate the new fault datasets in a step-wise manner...' is repeated almost verbatim for BNN-2, BNN-3, and BNN-4; this should be rewritten or condensed.
[Section III-A, Eq. (2)] The softmax loss in Eq. (2) uses the notation \hat p without defining it as the softmax output; define \hat p_d = exp(\hat y_d)/(sum_{d'} exp(\hat y_{d'})) before the loss expression.
[References] Reference [14] is a Master's thesis and reference [19] has a typo 'hhttps'; please provide a stable URL or DOI and correct the typo.

Circularity Check

2 steps flagged · score 6.0 of 10

BNN's unseen-fault uncertainty is a definitional consequence, and incremental-retraining gains are built into the supervised procedure.

self definitional [Section IV, Fig. 6 caption; Section I Introduction and Section I-A]
"These uncertainties arise when a mismatch between the testing and training data is encountered. ... However, a big discrepancy arises for an unseen fault where the uncertainty in BNN predictions rises to an alarming value."

The eccentricity fault is defined as 'unseen' because it is excluded from the training set, and the paper defines epistemic uncertainty as arising from inadequate knowledge and from a training/testing mismatch. Therefore, the finding that this particular excluded fault produces high BNN uncertainty is a restatement of the paper's own definition of uncertainty rather than an independent validation of the trustworthy-diagnosis alarm. No pre-specified threshold is given for 'alarming value,' so the demonstration cannot fail once 'unseen' is equated with 'uncertain.'
fitted input called prediction [Section IV, Fig. 8(a)-(c)]
"BNN–2 is updated with fault 3 in its seen environment, which automatically minimizes the diagnostic uncertainty and accurately identifies fault 3 (root crack fault) into the correct category, as opposed to the wrong predictions made initially in Fig. 6."

This is a supervised retraining step: fault 3 is moved from the unseen set into the labeled training set, so the model's 'accurate identification' of fault 3 is built into the optimization objective. Presenting this as an emergent BNN uncertainty-minimization result is treating the necessary consequence of adding the class to the training data as a prediction. The same structure repeats for faults 4 and 5 via BNN-3 and BNN-4, so each claimed step-wise improvement is guaranteed by construction.

full rationale

This paper is not built on load-bearing self-citations: Bayes-by-backprop [18], variational inference [17], and the softmax likelihood are external, standard components, and the authors' own prior works [1], [2], [14] are not used to force the central conclusion. However, the two central demonstrations are circular in the weaker but real sense. First, the paper defines uncertainties as arising from a training/testing mismatch and defines eccentricity as 'unseen'; showing that the unseen fault has high BNN uncertainty is a restatement of that definition, not a test of the proposed trustworthy-diagnosis gate. Second, the step-wise 'improvements' in Fig. 8 are guaranteed by moving the previously unseen fault into the labeled training set and retraining; accurate classification of the newly added class is built into the supervised objective. The absence of any numerical accuracy or calibration reporting compounds the issue but is a correctness/validation gap rather than circularity itself. Given that the incremental-retraining results reduce by construction and the headline unseen-fault uncertainty is definitional, a partial-circularity score of 6 is appropriate.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central result rests on standard Bayesian inference machinery plus an assumed link between the chosen input signals and fault classes. No new entities or fitted scientific constants are introduced; the free parameters are all network hyperparameters. The key unverified premises are that the variational posterior and the selected features yield well-calibrated uncertainty.

free parameters (4)

hidden layer sizes = 128 and 64
Network architecture in Fig. 5(b) was chosen by hand; no ablation or justification is provided.
learning rate = 0.001
Chosen by hand for stochastic gradient descent; no sensitivity analysis is reported.
Bayes-by-backprop prior hyperparameters = not stated
Prior distribution and its scale for the variational weights are not reported, so the uncertainty estimates depend on unspecified settings.
Monte Carlo sample count = not stated
The number of weight samples used to estimate the predictive distribution in Eq. (5) is not reported.

assumptions (4)

standard math Bayes' theorem and softmax likelihood for classification
Used in Section III-B, equations (4)-(6); standard probability and classification assumptions.
domain assumption The variational posterior q_theta(omega) faithfully approximates p(omega|X,Y)
Invoked in Section III-B/C via Bayes-by-backprop; no convergence or calibration diagnostics are provided.
domain assumption Input features Vdc, Isx, Isy carry enough information to separate gear fault classes
Section II-B notes overlapping distributions for torque and DC voltage, yet these features are used as BNN inputs in Figs. 7-8.
standard math Fault classes are mutually exclusive and softmax output is the correct likelihood form
Section III-B, equation (4); standard classification assumption.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives." pith.science (2026). https://pith.science/paper/ESFLO5HE

@misc{pith2026241201272,
  author       = {Pith},
  title        = {Pith review of: Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ESFLO5HE}},
  note         = {Machine review of arXiv:2412.01272}
}

read the original abstract

This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN). Conventional data-driven approaches used for fault diagnosis often rely on point-estimate neural networks, which merely provide deterministic outputs and fail to capture the uncertainty associated with the inference process. In contrast, BNNs offer a principled framework to model uncertainty by treating network weights as probability distributions rather than fixed values. It offers several advantages: (a) improved robustness to noisy data, (b) enhanced interpretability of model predictions, and (c) the ability to quantify uncertainty in the decision-making processes. To test the robustness of the proposed BNN, it has been tested under a conservative dataset of gear fault data from an experimental prototype of three fault types at first, and is then incrementally trained on new fault classes and datasets to explore its uncertainty quantification features and model interpretability under noisy data and unseen fault scenarios.

Figures

Figures reproduced from arXiv: 2412.01272 by the authors.

**Figure 1.** (a) Schematic view of the categorical differences between aleatoric and epistemic uncertainty – the former is aimed at noisy diverging data and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Out of distribution (OOD) samples correspond to the unseen [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Gearbox Dynamics Simulator used for collecting fault data. Detailed [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Fault signatures for the same loading profile – surface fault vs. no [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]

**Figure 5.** Figure 5: (a) Probabilistic assignment of neuronal weights to formalize a [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: Diagnosis of the testing samples of seen domain and unseen fault with benchmarking models and probabilistic Bayesian NN: For the extrinsic [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 8.** Figure 8: Performance evaluation of updated seen datasets by transforming BNN [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

**Figure 7.** Figure 7: Uncertainty estimation and decomposition using BNN for the testing [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 9.** Figure 9: Bayesian neural networks is a promising solution, that offers a multi [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 17 canonical work pages

[1]

S. Zhao, F. Blaabjerg and H. Wang, ”An Overview of Artificial Intelligence Applications for Power Electronics,” IEEE Trans. Power Electron., vol. 36, no. 4, pp. 4633-4658, April 2021

work page 2021
[2]

Sahoo, H

S. Sahoo, H. Wang, and F. Blaabjerg, ”On the Explainability of Black Box Data-Driven Controllers for Power Electronic Converters,” 2021 IEEE Energy Conversion Congress and Exposition (ECCE) , Vancouver BC, Canada, 2021

work page 2021
[3]

Zio, ”Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice” Reliab Eng Syst Saf , vol

E. Zio, ”Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice” Reliab Eng Syst Saf , vol. 218, pp. 108119, 2022

work page 2022
[4]

Y . Hu, X. Miao, Y . Si, E. Pan, E. Zio, ”Prognostics and health management: A review from the perspectives of design, development and decision”, Reliab Eng Syst Saf , vol. 217, pp. 108063, 2022

work page 2022
[5]

J. Jiao, M. Zhao, J. Lin, K. Liang, ”Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings” Reliab Eng Syst Saf, vol. 184, pp. 41-54, 2019

work page 2019
[6]

Y . Qin, Y . Mao, B. Tang, Y . Wang, H. Chen, ”M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems”, Mech Syst Signal Process, vol. 134, pp. 106298, 2019

work page 2019
[7]

Y . Lei, B. Yang, X. Jiang, F. Jia, N. Li,A. Nandi, ” Applications of machine learning to machine fault diagnosis: A review and roadmap”, Mech Syst Signal Process , vol. 138, pp. 106587, 2020

work page 2020
[8]

W. Mao, W. Feng, Y . Liu, D. Zhang, X. Liang, ”A new deep auto- encoder method with fusing discriminant information for bearing fault diagnosis”, Mech Syst Signal Process , vol. 150, pp. 107233, 2021

work page 2021

Show all 19 references

[9]

B. Zhao, X. Zhang, H. Li, Y . Yang, ” Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions”, Knowl-Based Syst, vol. 199, pp. 105971, 2020

2020
[10]

B. Han, S. Ji, J. Wang, H. Bao, X. Jiang, ”An intelligent diagnosis framework for roller bearing fault under speed fluctuation condition”, Neurocomputing, vol. 420, pp. 171-180, 2021

2021
[11]

D. Wang, Y . Chen, C. Shen, J. Zhong, Z. Peng, C. Li, ”Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring”, Mech Syst Signal Process, vol. 168, pp. 108673, 2022

2022
[12]

T. Li, Z. Zhao, C. Sun, L. Cheng, X. Chen, R. Yan, R. Gao, ”WaveletK- ernelNet: An interpretable deep neural network for industrial intelligent diagnosis”, IEEE Trans Syst Man Cybern , pp. 1-11, 2021

2021
[13]

T. Zhou, T. Han, and E. L. Droguett, ”Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework”Reliability Engineering & System Safety , vol. 224, no. 108525, 2022

2022
[14]

Biswas, ”Intelligent motor fault detection”, Master’s thesis, Univer- sity of South Denmark, 2023

A. Biswas, ”Intelligent motor fault detection”, Master’s thesis, Univer- sity of South Denmark, 2023

2023
[15]

Mukhoti, Y

J. Mukhoti, Y . Gal, ”Evaluating Bayesian deep learning methods for semantic segmentation”, arXiv preprint arXiv:1811.12709 , 2018

2018 arXiv
[16]

Rumelhart, G.E

D.E. Rumelhart, G.E. Hinton, R.J. Williams, ”Learning Internal Repre- sentations by Error Propagation”, Tech. Rep., California Univ San Diego La Jolla Inst for Cognitive Science , 1985

1985
[17]

L. V . Jospin, H. Laga, F. Boussaid, W. Buntine and M. Bennamoun, ”Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users,” IEEE Computational Intelligence Magazine , vol. 17, no. 2, pp. 29-48, May 2022

2022
[18]

Blundell, J

C. Blundell, J. Cornebise, K. Kavukcuoglu, D. Wierstra, ”Weight un- certainty in neural networks”, arXiv preprint arXiv:1505.05424 , 2015

2015 arXiv
[19]

Last accessed 24 Nov, 2024

AI-Power, hhttps://www.ipower.ai/. Last accessed 24 Nov, 2024

2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

S. Zhao, F. Blaabjerg and H. Wang, ”An Overview of Artificial Intelligence Applications for Power Electronics,” IEEE Trans. Power Electron., vol. 36, no. 4, pp. 4633-4658, April 2021

work page 2021

[2] [2]

Sahoo, H

S. Sahoo, H. Wang, and F. Blaabjerg, ”On the Explainability of Black Box Data-Driven Controllers for Power Electronic Converters,” 2021 IEEE Energy Conversion Congress and Exposition (ECCE) , Vancouver BC, Canada, 2021

work page 2021

[3] [3]

Zio, ”Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice” Reliab Eng Syst Saf , vol

E. Zio, ”Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice” Reliab Eng Syst Saf , vol. 218, pp. 108119, 2022

work page 2022

[4] [4]

Y . Hu, X. Miao, Y . Si, E. Pan, E. Zio, ”Prognostics and health management: A review from the perspectives of design, development and decision”, Reliab Eng Syst Saf , vol. 217, pp. 108063, 2022

work page 2022

[5] [5]

J. Jiao, M. Zhao, J. Lin, K. Liang, ”Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings” Reliab Eng Syst Saf, vol. 184, pp. 41-54, 2019

work page 2019

[6] [6]

Y . Qin, Y . Mao, B. Tang, Y . Wang, H. Chen, ”M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems”, Mech Syst Signal Process, vol. 134, pp. 106298, 2019

work page 2019

[7] [7]

Y . Lei, B. Yang, X. Jiang, F. Jia, N. Li,A. Nandi, ” Applications of machine learning to machine fault diagnosis: A review and roadmap”, Mech Syst Signal Process , vol. 138, pp. 106587, 2020

work page 2020

[8] [8]

W. Mao, W. Feng, Y . Liu, D. Zhang, X. Liang, ”A new deep auto- encoder method with fusing discriminant information for bearing fault diagnosis”, Mech Syst Signal Process , vol. 150, pp. 107233, 2021

work page 2021