Abstract Deep learning (DL) has attained remarkable achievements in diagnosing faults for rotary machineries. Capitalizing on the formidable capacity of DL, it potential to automate human labor and augment efficiency fault diagnosis machinery. These advantages have engendered escalating interest over past decade. Although recent reviews literature encapsulated utilization DL rotating machinery, they no longer encompass introduction novel methodologies emerging directions as continually...

The increasing demand for reliable wind turbine performance has highlighted the critical need advanced anomaly monitoring systems. Existing strategies are often found to struggle with efficient extraction and fusion of multi-attribute data, which essential ensuring secure operation turbines. In response, a novel approach, Multi-attribute Information Segmentation Fusion Network (MISFN), is proposed enhance through spatiotemporal feature from Supervisory Control Data Acquisition (SCADA) this...

Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome difficulties in this field. In most engineering scenarios, machines perform normal conditions, which implies that fault data may be hard acquire limited. Therefore, imbalance deficiency labels practical challenges bearings. Among mainstream methods, transfer learning-based highly...

Recently, deep learning has made brilliant achievements in wind turbine bearing fault diagnosis field. However, there are two problems that cannot be ignored: 1) the data is so scarce it time-consuming to acquire a well-behaved model; 2) much unlabeled adequately utilized explore useful information without prior. Therefore, novel semi-supervised temporal meta method (SSTML) proposed, which can not only probe representative features from massive raw vibration adequately, but also make best of...

Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) the 1.5-dimensional Teager kurtosis spectrum. The signal deconvoluted using MOMEDA method. analyzed by applying Finally, frequency characteristics were extracted for bearing fault. A...

The health of rolling bearings is related to the normal operation rotating machinery. Accurately predicting remaining useful life (RUL) key avoiding failure and system. In this paper, a new dynamic convolution Transformer model with ProbSparse self-attention mechanisms proposed extract advanced degradation characteristics from complicated vibration signal for accurately RUL bearing, which called (DPT) model. First, cumulative amplitudes frequency domain are computed as network inputs. Then,...

The paper focuses on the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> fault detection problem for a class of networked systems with intermittent measurements. filter (FDF) design is formulated as an filtering by using FDF. random packet dropouts, which are described Bernoulli distributed sequence, considered to exist in communication channels. dropout rate (PDR) uncertain and variable, Markov stochastic process. Based mode-dependent...

BACKGROUND: The aims of this study were to investigate the association between risk developing exercise myocardial ischemia and central obesity in people aged 40-70 years, provide a scientific reference for with obesity, theoretical basis cardiovascular studies.METHODS: Data obtained from persons years old July 2020 2021, who subsequently selected, data on health status, behaviors, basal tests, tests collected include valid sample 190 persons. χ2 test binary logistic regression analysis...

Fault detection in distribution networks is an important means of ensuring the safe operation power grids. With expansion scale networks, fault has become more challenging context network state monitoring. In order to accurately detect faults and issue timely warnings, a data-driven method based on deep learning with self-attention mechanism proposed. The multiple convolution kernels different sizes are utilized extracted local features at scales. At same time, Bidirectional Long Short-Term...

Effective anomaly monitoring is crucial for ensuring the safe operation of wind turbines, necessitating advanced and data collection techniques. The supervisory control acquisition (SCADA) technology, recording a range parameters relevant to turbine operation, fundamental in this regard. In paper, combinatorial network named temporal convolutional network-transformer (TCN- Transformer) proposed extract multidirectional features SCADA condition monitoring. Firstly, cleaned with greater...

The stable operation of the power distribution network plays an important role in ensuring reliability and continuity transmission. However, often experiences faults such as equipment failures line short circuits during its operation. These not only affect normal but also result significant social economic losses. Therefore, a new fault detection method which is temporal convolutional (TCN) cascaded with long short-term memory (LSTM) parallel (TLPN) proposed. feeder current adopted input...

PURPOSE: To study the effect of weight-bearing running on Nlrp3 inflammasome by observing changes related proteins expression extensor digitorum longus in aged rats after 16-weeks intervention. METHODS: Thirty healthy 8-month-old male SD were randomly divided into a control group (Group C, n = 15) and R, 15). Five each taken as basic value before Group R was given intervention, 3 times per week. Animals weighed, body compositions measured, morphology muscle observed HE staining, protein...