Paper proposes a predictive maintenance system for solar plants using big data.
problem Fault prediction in photovoltaic plants to reduce downtime and maintenance costs.
method Data-driven approach with unsupervised clustering and Pattern Recognition Neural Network.
result Effective prediction of both generic and specific faults, up to 7 days in advance.
Generative model predicts vehicle faults up to 1000 hours in advance.
problem Forecasting vehicle faults for predictive maintenance.
method Generative model trained on US Army data, incorporating real-world factors.
result Highly accurate predictions of time to first fault.
Paper tackles fault classification in time series data with deep neural networks.
problem Fault classification over a future horizon in multidimensional time series data with class imbalances.
method Proposes a multi-label recurrent neural network with a new cost function to address class imbalances.
result The proposed algorithm outperforms state-of-the-art techniques in F1-score, precision, and recall.
TS-Fault benchmarks TSF models against structural faults.
problem Evaluating the robustness of time series forecasting models against structured events.
method TS-Fault uses parameterized fault scenarios with controllable difficulty.
result Three findings contradict common leaderboard intuition.
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment. This paper proposes an effective machine learning algorithm to predict industrial…
Neural network predicts electrochemical cell faults with 53% less error.
problem Predicting faults in electrochemical cells to avoid safety hazards and reduce costs.
method Self-supervised encoder-decoder neural network that learns degradation from operating conditions.
result Predicted voltage with 53% less error than parametric models, 64% faster fault prediction.
New method improves fault detection by adding unsupervised learning to Monte Carlo dropout models.
problem Detecting and diagnosing incipient and unknown faults in deep neural networks.
method Augmenting Monte Carlo dropout models with unsupervised learning tasks.
result Improved fault detection and diagnosis performance, especially on out-of-distribution examples.
Machine learning models predict earthquake rupture dynamics efficiently.
problem Challenges in simulating earthquake rupture dynamics due to uncertainties in physics.
method Developed two machine learning models (ANN and RF) to predict rupture propagation using a database of 1600 simulations.
result Both RF and ANN models predict rupture propagation with over 81% accuracy and can infer important factors for rupture.
Study designs neural networks for fault localization, state estimation, and optimal PMU placement in power systems.
problem Fault localization, state estimation, and optimal PMU placement in power systems.
method Designs and compares various neural networks for fault localization, builds machine learning schemes for state estimation and parameter estimation, and designs an algorithm for optimal PMU placement.
result Comprehensive comparison of neural networks for fault localization shows that Graphical Convolutional NN and Neural Graph-based ODE perform best.
New method detects bearing faults using multivariate statistical process control.
problem Early detection of bearing faults in rotating machinery.
method Multivariate statistical process control charts applied to Fourier transform features of fixed-time batches.
result Effectiveness in detecting bearing faults across different conditions.
We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and cau…
Semi-supervised method predicts faults in systems with multiple operation modes.
problem Fault detection in industrial systems with missing target data.
method Semi-supervised soft sensor modeling incorporating mode transition properties.
result Regression coefficients estimated under constraint conditions from mode transitions.
Paper addresses class imbalance in disk SMART dataset using GANs and genetic algorithms.
problem Class imbalance in disk SMART dataset.
method Data synthesised by multivariate GANs mixed with genetic algorithms.
result Higher disk fault classification prediction accuracy.
Develops a data-driven fault diagnosis framework for time-series data.
problem Fault diagnosis of dynamic systems using imbalanced and unknown fault classes.
method Kullback-Leibler divergence, data-driven fault classification, open-set classification.
result Framework handles imbalanced datasets, class overlapping, and unknown faults.
CAT improves domain adaptation for fault diagnosis by calibrating teacher network predictions.
problem Performance drops in deep learning models when applied to different data distributions.
method CAT uses post-hoc calibration techniques to calibrate predictions of the teacher network during self-training.
result CAT achieves state-of-the-art performance on most transfer tasks in domain-adaptive IFD.
Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happ…
Non-availability of reliable and sustainable electric power is a major problem in the developing world. Renewable energy sources like solar are not very lucrative in the current stage due to various uncertainties like weather, storage, land use among others. There also exists various other issues like mis-commitment of…
Neural network residuals isolate and locate unknown faults.
problem Locating unknown faults in industrial systems.
method Neural network-based residuals combining physical insights and machine learning.
result Neural network residuals can isolate and locate unknown faults.
Meta-reinforcement learning improves fault-adaptive control efficiency.
problem Adaptive control under abrupt system faults with strict time constraints.
method Model-agnostic meta learning (MAML) with a fault library of prior policies.
result Improved sample efficiency and quick adaptation to new faults.
Ensemble models struggle with detecting mild faults.
problem Difficulty in detecting Intermediate-Severity faults due to their resemblance to normal conditions.
method Extensive experiments with ensemble models to identify and address common pitfalls.
result Designing more effective ensemble models for IS fault detection and diagnosis.
Proposes KIL-AdaVAE for fault detection and segmentation of unknown fault types.
problem Lack of labeled data for fault types in safety-critical systems.
method Implicit supervision with Deep Variational Autoencoders (VAE).
result Significant performance improvements in fault detection and segmentation.
Ranger improves DNNs' fault resilience without re-computation.
problem Transient faults in DNNs cause errors, reducing reliability.
method Range restriction to transform critical faults to benign faults.
result Significant improvement in error resilience (3x to 50x) with no accuracy loss.
Residual generation helps diagnose engine faults using neural networks.
problem Fault diagnosis in engines with unknown classes and limited data.
method Grey-box recurrent neural networks incorporating physical insights.
result Improved fault classification and root cause identification.
DriveFI uses ML to find critical faults in AVs, saving time and resources.
problem Lack of end-to-end fault assessment in AVs under realistic scenarios.
method Machine learning-based fault injection engine (DriveFI) that identifies safety-critical faults.
result Found 561 safety-critical faults in less than 4 hours, compared to weeks of random injection.
Novel method detects faults in helicopter transmissions using healthy data only.
problem Detecting faults in rare events in helicopter transmissions.
method Probabilistic anomaly detection using Bayesian measures.
result Method achieves competitive detection performance in real-world and benchmark applications.
Proposes a model to update industrial data predictions based on temporal changes.
problem Improving prediction accuracy in industrial data analytics by addressing changing conditions over time.
method Integrates similarity and loss functions to estimate and update prediction models adaptively.
result The data renewal model enhances prediction accuracy by identifying and updating model changes.
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
problem Real-time price prediction for high-volatility cryptocurrencies.
method Spark-based architecture, sentiment analysis, online learning.
result Real-time adaptation of learning algorithms to new data.
Paper proposes FTT-NAS to create fault-tolerant CNNs for edge devices.
problem Faults in edge devices affect deep learning applications.
method Formalized fault models, implemented FTT-NAS, incorporated FTT.
result Discovered CNNs outperform baseline architectures with fault tolerance.
Paper proposes using MC-dropout to detect and diagnose incipient faults in buildings.
problem Lack of labeled incipient fault data in buildings.
method Proposes using Monte Carlo dropout (MC-dropout) to enhance deep neural networks for fault detection.
result Demonstrates effectiveness of MC-dropout in indicating likely incipient fault types.
We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …
Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.
problem Traditional machine learning models struggle to predict disk failures due to limited data.
method Multi-layer domain adaptive learning with source and target domains.
result The proposed method improves failure prediction accuracy on disk data with few failure samples.
Quantum computing improves fault diagnosis in industrial processes.
problem Fault detection and diagnosis in industrial process systems.
method Integrates quantum computing and deep learning to extract features and diagnose faults.
result Quantum-assisted deep learning achieves high fault detection rates (79.2% and 99.39%).
Mitigates faults in DNNs by clipping activation values, improving their resilience.
problem Fault tolerance of DNNs in safety-critical applications.
method Clipping activation functions to reduce impact of faulty weights.
result Significant improvement in classification accuracy (68.92%) for fault mitigation.
New taxonomy for SCADA-based wind turbine fault detection improves model performance.
problem Lack of consensus on feature causality in normal behavior models.
method Presented a new taxonomy based on causal relations between input features and target.
result Evaluation of different feature configurations on fault detection performance.
Deep learning diagnoses rotary machine faults without expert input.
problem Early detection of faults in rotary machinery to save time and money.
method Deep Convolutional Neural Network with three axis accelerometer signal input.
result High classification accuracy in fault diagnosis.
Bayesian Recurrent Neural Networks improve fault detection and identification in manufacturing.
problem Detect and identify faults in chemical processes to ensure optimal operations.
method Bayesian Recurrent Neural Networks (BRNNs) with variational dropout.
result BRNNs provide uncertainty estimates for fault detection and identification.
Few-shot learning improves bearing fault diagnosis with limited data.
problem Challenges in collecting sufficient fault data for robust classifier training.
method Model-Agnostic Meta-Learning (MAML) for few-shot learning.
result Framework achieves up to 25% higher accuracy than Siamese network.
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…
A stealthy framework injects faults into DNNs to misclassify images without affecting overall accuracy.
problem Vulnerability of deep neural networks to misclassification attacks.
method Fault sneaking attack using ADMM optimization with constraints on maintaining model accuracy and minimizing parameter modifications.
result The framework can inject multiple sneaking faults into DNNs without reducing overall accuracy.
Domain-Adversarial Neural Networks improve fault diagnosis models across different machines.
problem Improving fault diagnosis models on new machines with limited labeled data.
method Domain-Adversarial Neural Networks (DANN) and other methods for domain adaptation.
result Unified experimental protocol for fair comparison of domain adaptation methods.
TensorFI injects faults in TensorFlow programs to assess their reliability.
problem Ensuring reliability of machine learning systems in safety-critical domains.
method TensorFI is a flexible fault injection framework for TensorFlow applications.
result TensorFI evaluates the resilience of 12 ML programs, including autonomous vehicle DNNs.
This review explores Convolutional Neural Networks in machine fault diagnosis.
problem Machine fault diagnosis is crucial for safe equipment operation and production.
method Comprehensive review of Convolutional Neural Network (CNN) applications in fault diagnosis.
result A systematic review of CNN-based fault diagnosis methods, covering data collection, model construction, and feature learning.
New PCA method detects faults using occupation kernels.
problem Fault detection in dynamical systems.
method Occupation kernel PCA for irregularly sampled data.
result Validation of reconstruction error approach for fault detection.
Paper addresses fault-tolerance in distributed machine learning with stochastic gradient descent.
problem Fault-tolerance in distributed stochastic gradient descent (D-SGD) for machine learning.
method Proposes norm-based comparative gradient elimination (CGE) to robustify D-SGD against Byzantine faulty agents.
result CGE guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions.
Survey of ML and DL for bearing fault diagnostics.
problem Fault detection and categorization in bearings.
method Review of conventional ML methods and analysis of DL algorithms.
result DL methods outperform conventional ML in fault feature extraction and classification.
Prototype for early fault warnings in large electric grids.
problem Early detection and classification of faults in complex electric grids.
method Multi-stage approach with anomaly detection, feature mapping, classification, and clustering.
result Random forest method offers the most accurate fault classification.
RODMAN improves ML-based disk failure prediction accuracy in cloud environments.
problem Imperfect data quality in real-world cloud environments degrades ML-based disk failure prediction accuracy.
method RODMAN uses three data preprocessing techniques: failure-type filtering, spline-based data filling, and automated pre-failure backtracking.
result RODMAN significantly improves prediction accuracy compared to no preprocessing.
The paper investigates how reducing memory supply voltage improves DNN accuracy under bit-cell faults.
problem Reducing energy consumption in deep neural networks by lowering memory supply voltage introduces bit-cell faults.
method The authors explore the robustness of DNN architectures to bit-cell faults and propose a regularizer to mitigate their effects.
result Operating the system in a faulty regime can save energy without significantly reducing accuracy.