The faults of rolling bearings can result in the deterioration rotating machine operating conditions, how to extract fault feature parameters and identify bearing has become a key issue for ensuring safe operation modern machineries. This paper proposes novel hybrid approach random forests classifier diagnosis bearings. are extracted by applying wavelet packet decomposition, best set mother wavelets signal pre-processing is identified values signal-to-noise ratio mean square error. Then,...

Under nonlinear and nonstationary dynamic conditions, the fault diagnosis methods based on multidimensional dimensionless indicators (MDIs) often cannot provide effective accurate health monitoring in of petrochemical units. In view above problems, this article preprocesses signal reconstructs a new indicator. The indicator combines complementary ensemble empirical mode decomposition (CEEMD) with MDI, named multidimensionless (CEMDIs). By using sequential mapping method, CEMDI processed data...

Since data fusion in the process of traditional fault diagnosis method is not accurate enough, it difficult to use dimensionless index distinguish among types problems. This paper proposes a based on mutual dimensionless. uses real-time acquisition original and calculations, obtains five indices for each dataset, then support vector machine (SVM) model projections dataset judge types. Using raw data, SVM training can more effectively solve problem due imperfection old leading low accuracy...

As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and local minimum of new is proposed based on simulated annealing (SA) genetic (GA). The combined utilizes due its search abilities. Thereby, problems associated with algorithm, such as tendency prematurely select optimal values, can be overcome, applied analysis. Moreover, solve other which include...

In this paper, the advantages of dimensionless indices and two types correlation coefficients are combined methods proposed to enhance efficiency accuracy fault diagnosis in petrochemical rotating machinery. The order statistic coefficient Pearson's used calculate indices, which given by algorithms after preprocessing raw data. Different recognized comparing each indicator. numerical results revealed that method has highest 80% while average 50%, an overall improvement 10.89% compared with...

In view of the shortcomings traditional fault diagnosis methods based on time domain vibration analysis, which require complicated calculations feature vectors, and are over-dependent a prior knowledge, effort for design extraction algorithms, have limited ability to extract complex relationships in signals, this paper we propose convolutional neural network (CNN) framework machine health monitoring encoding one-dimension (1-D) series two-dimension (2-D) images. This defines new Gram matrix...

Childhood asthma is the most universal chronic disease, with significant cases reported. Despite current progress in treatment, prognosis remains poor and existing drugs cause serious side effects. This investigation explored mechanisms use of miR-335-5p on childhood therapy. MiR-335-5p ATG5 expression was analyzed clinical plasma samples through RT-qPCR. Airway smooth muscle cells (ASMCs) were cultured, transfected mimic, inhibitor, pcDNA3.1-ATG5, or co-transfected mimic + pcDNA3.1-ATG5....

The bearing fault diagnosis of petrochemical rotating machinery faces the problems large data volume, weak feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint reduction, mutual dimensionless metrics MPGA-SVM approach for diagnosis. Firstly, we propose an improved noise-reduction method Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) wavelet...

This paper proposes a double sample data fusion method based on combination rules to improve the classification of dimensionless indices in petrochemical rotating machinery equipment. first collects original and counts mutual index as body evidence. The reliability evidence is then determined using distance calculation method. Finally, reasoning used fuse reliability, type fault detected K-S test. A real-time collection experiment shows that this can identify for have appearance coincidences...

Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and lack systems theory relating parameter selection. In this paper, a optimization algorithm for SVM is proposed based multi‐genetic algorithm. The optimizes correlation kernel using evolutionary search principles multiple swarm genetic obtain superior prediction model. experimental results demonstrate that by combining algorithm, fault...

Rotating machinery plays a pivotal role in petrochemical units. However, compound and single faults frequently occur rotating due to the complexity of operating environments coupling faults. This paper presents new fault diagnosis method address problem poor effect caused by mutual interference between multiple responses. Firstly, observable signals are decomposed via Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Wavelet threshold Filtering Reconstruction (FR)...

Along with the continuous development of science and technology, structures rotating machinery become to be larger scale more complicated, which results in higher probability concurrent fault under actual working conditions. In order achieve diagnosis for machinery, an integrated method using artificial immune algorithm evidence theory is proposed this research work. The self-nonself recognition mechanism system data analysis processing has been derived from negative selection algorithm....

We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel data-driven approach achieve PMS using condition monitoring information degradation data of such system under dynamic operating scenario. In this sense, paper differs from existing methods only considering static scenario without real-time information, which aims estimate population but not an individual. presented approach, establish linkage between historical...

Vehicle routing problem with soft time windows (VRPSTW) is represented as a multi-objective optimization which both considering the number of vehicles and total cost (distance). We simultaneously propose an improved genetic algorithm to solve this problem. In algorithm, we by variation fitness function. are not only increase search ability but also satisfied requirement population diversity using crossover operator. add local make complete for deficiency weak ability. The experiment result...

In this paper, to satisfy the need of fault monitoring, dynamic real time vibration monitoring and signal analysis for large machine unit in petrochemical industry, which cannot realize real-time, online fast diagnosis, an intelligent diagnosis system is developed using artificial immune algorithm dimensionless indicators, innovated with a focus on reliability, remote practicality, be applied Third Catalytic Flue Gas Turbine enterprise have got good effects.

Retinal vessel segmentation is crucial for diagnosing and monitoring ophthalmic systemic diseases. Optical Coherence Tomography Angiography (OCTA) enables detailed imaging of the retinal microvasculature, but existing methods OCTA face significant limitations, such as susceptibility to noise, difficulty in handling class imbalance, challenges accurately segmenting complex vascular morphologies. In this study, we propose VDMNet, a novel network designed overcome these by integrating several...

In this paper, we propose a scheduling method based on real-time information of the scene to solve flexible job shop problem. The model is described in detail, mechanism, machine selection and are established relatively. Then problem by using algorithms validate it some examples.

The prediction of the remaining useful life (RUL) rolling bearings is a pivotal issue in industrial production. A crucial approach to tackling this involves transforming vibration signals into health indicators (HI) aid model training. This paper presents an end-to-end HI construction method, vector quantised variational autoencoder (VQ-VAE), which addresses need for dimensionality reduction latent variables traditional unsupervised learning methods such as autoencoder. Moreover, concerning...