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Article
Engineering, Mechanical
Rong Zhu et al.
Summary: This paper presents a Bayesian semi-supervised transfer learning with active querying-based intelligent fault prognostic framework for predicting remaining useful life (RUL) across different machines with limited data, integrating the advantages of transfer learning and active learning in the Bayesian deep learning framework. Bayesian neural networks with Monte Carlo dropout inference are used to quantify RUL prediction uncertainty and an active querying-based training data selection mechanism is developed. Transfer learning is simultaneously embedded to address data distribution discrepancies among the different machines.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Zhenan Pang et al.
Summary: This paper proposes a condition-based prognostic approach for age-and state-dependent partially observable nonlinear degrading system. The proposed approach characterizes the dynamics and nonlinearity of the system degradation process using age-and state-dependent nonlinear diffusion process and state space model. The distribution of the remaining useful life is derived using extended Kalman filtering and expectation-maximization algorithm, and can be updated in real-time with new data.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Automation & Control Systems
Zhiyi He et al.
Summary: This article proposes a modified deep autoencoder method driven by multi-source parameters for fault prognosis of aeroengines. The method utilizes a fused health index to characterize performance degradation and establishes accurate mapping hidden in the health index using adaptive Morlet wavelet. Parameter transfer learning is used to enable the model to have cross-domain fault prognosis capability.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Xingchen Liu et al.
Summary: This article develops a novel condition monitoring and fault isolation system for wind turbines based on SCADA data. The article addresses challenges such as low sampling rate, time-varying working conditions, and lack of historical fault data. The system uses preprocessing and a global monitoring statistic to monitor the health status of the wind turbine and isolate faults without expert knowledge or historical data.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Md Rafiul Hassan et al.
Summary: Conventional diagnostic techniques for cardiovascular autonomic neuropathy struggle with early or atypical stage identification due to incomplete data. A novel multi-class classification approach was proposed to enhance CAN detection accuracy, combining feature selection and multimodal feature fusion techniques. The experimental results indicated significant improvement in diagnostic accuracy compared to traditional methods, particularly for early or atypical stages of the condition.
INFORMATION FUSION
(2022)
Article
Engineering, Electrical & Electronic
Cheng-Geng Huang et al.
Summary: Existing fault prognostic methods based on deep learning require massive labeled data, which is not feasible for real machines. To overcome this limitation, a novel Bayesian deep dual network with domain adaptation is proposed to transfer fault prognosis across different machines with distinct structures and conditions.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Mechanical
Xuefeng Kong et al.
Summary: This paper proposes a flexible reliability analysis framework for multi-component systems, which utilizes a stochastic process-based degradation model and factor analysis to describe component degradation processes and interactions. By relaxing the assumption of identical degradation processes and preset dependency structures, the robustness of the method is enhanced, and the explicit form of the system reliability function is derived. The superiority of the method is demonstrated through two real case studies.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Liang Guo et al.
Summary: This paper proposes a method for constructing health indicators based on unsupervised feature learning, which calculates the health status of machines by building a multiscale convolutional autoencoder network and optimizing hyperparameters through genetic algorithms. Experimental results demonstrate that this method can effectively identify the degradation process of machines and achieve better performance.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Rui He et al.
Summary: This paper proposes a semi-supervised generative adversarial network (GAN) regression model for RUL predictions, considering both failure and suspension histories. The method can improve model generalization by matching statistical information for training, providing credibility in cases of scarce failure data.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Industrial
Rong Zhu et al.
Summary: This study proposes a Bayesian deep-active-learning framework for RUL prediction, which aims to achieve high accuracy with limited run-to-failure data. The method utilizes Bayesian neural networks for prediction and actively selects target samples for acquiring run-to-failure labels based on prediction uncertainty. A recursive model training and active data selection mechanism are developed to maintain accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Mathematics, Applied
Dario Fasino et al.
Summary: This work extends the known results of mean hitting and return times for standard random walks to the second-order case. By solving systems of linear equations, simple formulas are provided to compute these numbers. Furthermore, second-order versions of the famous Kac's and Random Target Lemmas are introduced by considering the 'pullback' first-order stochastic process of a second-order random walk.
EUROPEAN JOURNAL OF APPLIED MATHEMATICS
(2022)
Article
Engineering, Mechanical
Runhang Ge et al.
Summary: This study proposes a robust estimation method for sensor and measurement errors in data collection by modeling the errors using a Scale-Mixture Normal distribution. The proposed method incorporates an efficient algorithm to estimate model parameters and derive the remaining useful life distribution.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Junchuan Shi et al.
Summary: This paper proposes a physics-informed machine learning method for accurate modeling and prediction of the remaining useful life (RUL) of Lithium-ion batteries. The method considers the impact of battery health and operating conditions on battery aging and combines a calendar and cycle aging model with an LSTM layer for modeling and prediction. Experimental results demonstrate that the proposed method can accurately model and predict the degradation behavior and RUL of Lithium-ion batteries under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Joao Luiz Junho Pereira et al.
Summary: This work aims to develop a Structural Health Monitoring methodology that maximizes the acquired modal response and minimizes the number of sensors in a helicopter's main rotor blade. A new methodology, called MOSSPOLA, was proposed to address the Sensor Placement Optimization problem using the Multi-objective Lichtenberg Algorithm and Feature Selection. The results showed the correlation between sensor distributions and better metrics, and the MOSSPOLA found a sensor configuration with 100% accuracy in identifying delamination.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Statistics & Probability
Lu Lu et al.
Summary: The study introduces a multivariate general path model for analyzing degradation data with multiple degradation characteristics. The model incorporates random effects and covariates, and utilizes an expectation-maximization algorithm and Markov chain Monte Carlo simulation for parameter estimation and predicting system reliability.
Article
Computer Science, Artificial Intelligence
Haidong Shao et al.
Summary: Collaborative fault diagnosis can benefit from multisensory fusion technologies, which offer more reliable results with a comprehensive data set. By utilizing a stacked wavelet auto-encoder structure and flexible weighted fusion strategies, the proposed approach overcomes obstacles in integrating multisensory data and fusion of maintenance strategies, providing more accurate fault diagnosis results.
INFORMATION FUSION
(2021)
Article
Engineering, Mechanical
Dong Wang et al.
Summary: The study introduces a new family of sparse measures, Box-Cox Sparse Measures (BCSM), by generalizing the weights used in kurtosis and negative entropy through the introduction of Box-Cox transformation. The proposed BCSM aims to better quantify the sparsity of signals and experimental studies show its performance advantages.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Green & Sustainable Science & Technology
Dominik Putz et al.
Summary: Renewable energy sources, particularly wind power, have become a significant energy source in most power grids. This study introduces a powerful wind power forecasting tool based on the N-BEATS deep neural network approach, which outperforms established models and significantly reduces errors with a tailored loss function.
Article
Engineering, Mechanical
Bingxin Yan et al.
Summary: This study proposes a two-stage physics-based Wiener process model that integrates fatigue crack mechanisms, crack growth laws, and other minor factors, achieving high accuracy in remaining useful life (RUL) prediction. By jointly employing online change point detection, parameter estimation, and RUL prediction, a general prognostic framework is formulated with good statistical inference and applicability in general nonlinear systems. The joint implementation of an offline two-step parameter estimation method and online Bayesian update method allows for high precision RUL prediction.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Dongdong Liu et al.
Summary: This paper presents a novel intelligent cross-condition bearing fault recognition scheme, which utilizes the normalized resampled characteristic power (NRCP) feature constructed based on pulse-based order spectrums to develop a fault recognition strategy. The experimental results demonstrate the method's capability to differentiate different health conditions and types of faults in bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Energy & Fuels
Boris N. Oreshkin et al.
Summary: The proposed deep neural network modeling approach effectively solves the mid-term electricity load forecasting problem, outperforming all competitors in terms of both accuracy and forecast bias. It is simple to implement, does not require signal preprocessing, and is equipped with a forecast bias reduction mechanism.
Article
Engineering, Industrial
Xuefeng Kong et al.
Summary: A general degradation model considering random shock fluctuations and measurement uncertainty was developed to describe the degradation process, with a two-step approach and expectation-maximization algorithm used for parameter estimation. The effectiveness of the method was verified through numerical examples and practical case studies.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Statistics & Probability
Qiuzhuang Sun et al.
Article
Automation & Control Systems
Weiwen Peng et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2020)
Article
Engineering, Mechanical
Dong Wang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2020)
Article
Engineering, Electrical & Electronic
Karen Adam et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2020)
Article
Automation & Control Systems
Jun Zhu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2019)
Article
Automation & Control Systems
Siyu Shao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2019)
Article
Automation & Control Systems
Chuang Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2019)
Article
Engineering, Mechanical
Jinde Zheng et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2018)
Article
Engineering, Mechanical
Weiwen Peng et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2014)
Article
Engineering, Industrial
Linkan Bian et al.
Article
Computer Science, Artificial Intelligence
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2010)
Article
Statistics & Probability
J Freeman et al.
STATISTICS & PROBABILITY LETTERS
(2006)
