This paper presents a fault diagnosis technique for milling machines based on acoustic emission (AE) signals and a hybrid deep learning model optimized with a genetic algorithm. Mechanical failures in milling machines, particularly in critical components like cutting tools, gears, and bearings, account for a significant portion of operational breakdowns, leading to unplanned downtime and financial losses. To address this issue, the proposed method first acquires AE signals from the milling machine. AE signals, capturing the dynamic responses of machine components, are transformed into continuous wavelet transform (CWT) scalograms for further analysis. Gaussian filtering is applied to enhance the clarity of these scalograms, effectively reducing noise while maintaining essential features. A convolutional neural network (CNN) based on the VGG16 architecture is utilized for spatial feature extraction, followed by a bidirectional long short-term memory (BiLSTM) network to capture the temporal dependencies of the scalograms. The genetic algorithm (GA) is used to optimize feature selection and ensure the selection of the most relevant features to further improve the model’s performance. The optimized features are finally fed into a fully connected (FC) layer of the proposed hybrid model for fault classification. The proposed method achieves an accuracy of 99.6%, significantly outperforming traditional approaches. This method offers a highly accurate and efficient solution for fault detection in milling machines, allowing for more reliable predictive maintenance and operational efficiency in industrial settings. 

Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization

The existing intelligent monitoring methods are hard to apply directly to hydraulic machinery due to fault data collection difficulties and the uncertainty of fault occurrence. To monitor the hydraulic machinery effectively, we design a novel feature extraction method called TFF_VMD and propose a comprehensive framework based on support vector data description (SVDD) and support vector machine (SVM), dividing the monitoring task into anomaly detection and fault identification. A pump test bench is built for data collection and method validation. Meanwhile, one online bearing dataset is used to pre-train SVM. The proposed method achieves satisfying results on three datasets, getting the accuracy of anomaly detection at 0.891, 0.999, and 0.995, respectively, getting mean and standard deviation of fault diagnosis accuracy at 0.954 ± 0.05, 0.96 ± 0.04, and 0.98 ± 0.03. Results show that TFF_VMD is valuable for SVDD and SVM, and the proposed framework can preliminarily achieve early warning and diagnosis of mechanical and hydraulic faults. 

The study of hydraulic machinery condition monitoring based on anomaly detection and fault diagnosis

In order to solve the problem of environmental pollution control in the energy ecological economic zone, this paper proposes a technology to realize environmental pollution control by using big data. The main content of this technology is based on the characteristics and application of big data technology; through the collection of online monitoring data of ecological environment pollution, the construction of the online monitoring data quality model of ecological environment pollution, and finally through the experiments in the simulation environment, the feasibility of big data technology is obtained. The experimental results show that the average quality factor data of the quality control model in the environmental scientific data supervision is 2.16, the average quality factor data of the Internet of things in the marine pollution monitoring is 5.88, and the average quality factor data of the model in the ecological environment pollution online monitoring based on the big data technology is 8.64. Big data technology has good applicability. It is proven that the research of big data technology can meet the needs of environmental pollution control in the energy ecological economic zone.

https://downloads.hindawi.com/journals/itees/2022/1569905.pdf

Application of Big Data Technology in Environmental Pollution Control in Energy Ecological Economic Zone

A method is proposed for fault classification in milling machines using advanced image processing and machine learning. First, raw data are obtained from real-world industries, representing various fault types (tool, bearing, and gear faults) and normal conditions. These data are converted into two-dimensional continuous wavelet transform (CWT) images for superior time-frequency localization. The images are then augmented to increase dataset diversity using techniques such as rotating, scaling, and flipping. A contrast enhancement filter is applied to highlight key features, thereby improving the model’s learning and fault detection capability. The enhanced images are fed into a modified AlexNet model with three residual blocks to efficiently extract both spatial and temporal features from the CWT images. The modified AlexNet architecture is particularly well-suited to identifying complex patterns associated with different fault types. The deep features are optimized using ant colony optimization to reduce dimensionality while preserving relevant information, ensuring effective feature representation. These optimized features are then classified using a support vector machine, effectively distinguishing between fault types and normal conditions with high accuracy. The proposed method provides significant improvements in fault classification while outperforming state-of-the-art methods. It is thus a promising solution for industrial fault diagnosis and has potential for broader applications in predictive maintenance. 

Enhanced Fault Diagnosis in Milling Machines Using CWT Image Augmentation and Ant Colony Optimized AlexNet

A multi-source heterogeneous information perception method for power distribution facilities in communities is designed. It involves the collection of information about these facilities. This collection is accomplished using an improved YOLOv5 method. This method also includes the analysis of the correlation characteristics of information for distribution facilities. This analysis is conducted using the FP-Growth algorithm. The perception and recognition of multi-source heterogeneous anomaly information in distribution facilities are finally achieved. This is done through the extraction of features. This paper integrates data from different sources, which helps to improve the perception coverage rate. It also enhances the accuracy of data anomaly recognition in communities. In addition, this paper provides strong data support for accurate facility management and optimization decision-making. It effectively solves the problem of information perception of power distribution facilities in communities. This solution helps to build a more accurate anomaly detection model. Through feature extraction, the difference between normal and abnormal modes is identified. This identification enhances the information perception ability of power distribution facilities in communities. As a result, the perception and recognition of abnormal information are realized. Ultimately, the coverage of information perception for distribution facilities exceeds 90%, and achieves a precision of over 95% in identifying abnormal information related to community distribution facilities.

A Multi-Source Heterogeneous Information Sensing Method for Distribution Facilities in Communities

Abnormal identification of oil monitoring based on LSTM and SVDD

Power output is an important property of steam turbines and more accurate trend prediction is essential for understanding the operation of power plants and anomaly detection in equipment. Equipment power is strongly influenced by human factors and achieving accurate trend prediction is often impossible. In the current research, a continuous prediction model was developed on the basis of deep learning to establish the relationships among oil online monitoring parameters and equipment power. The model used long short term memory (LSTM) method to develop a trend prediction model for oil wear state parameters. Trend prediction results were applied as a test set to establish a deep belief networks (DBN) prediction model for predicting device power. In modeling process, continuous prediction model was optimized via feature selection method on the basis of ridge regression with L2 regularization, recursive feature elimination (RFE) and differential evolution (DE) algorithms for the elimination of subjective factor to decrease cumulative error of forecasting. Comparative experimental results showed that LSTM-RFE-DE-DBN continuous prediction model outperformed LSTM-RFE-DE-BPNN and LSTM-DBN. The developed model realized continuous prediction and applied lubricating oil wear status with objective factors to perform the power prediction of power plant turbines with subjective factors. 

Power plant turbine power trend prediction based on continuous prediction and online oil monitoring data of deep learning

In order to accurately understand the operating state of the equipment, monitor the abnormality of the oil state data in time, and effectively extract the abnormal data information in the oil monitoring data, this paper established a classification-driven SAE oil monitoring data abnormality recognition model. The nonlinear characteristics of the data predicted the state of the oil data. The label information is introduced into the collected oil monitoring data, and then the data is preprocessed. The deep features in the oil monitoring data are extracted by the stacked autoencoder (SAE). In the coding stage, the oil monitoring data training network with labels is used to realize the identification of abnormal data. The experimental results showed that: Compared with the Back Propagation Neural Network (BPNN) and the Support Vector Machine (SVM) classifier, the classification-driven stacked autoencoder had higher anomaly identification accuracy and could effectively detect abnormal data in oil monitoring data, so as to identified the abnormal monitoring of equipment status.

Abnormal Identification of Oil monitoring Data Based on Classification-Driven SAE

With the development of the times, the demand for high efficiency and reliability of machine performance in China's industry has become higher than ever before, and the traditional equipment condition evaluation method has also encountered a huge challenge. As an important branch of machine learning, cluster analysis is widely used in fault diagnosis and other fields because of its advantages of no prior knowledge and massive data processing. This paper introduces the concept of principal component analysis and K-means method based on time series in the field of oil data analysis. Through the test data on the oil monitoring data of the steam turbine unit in the power plant, the condition evaluation and classification of the equipment are carried out, among which the classification effect of 6μm particles, dielectric constant and 4μm particles is very obvious, and the water content is relatively obvious, which can basically distinguish each state. At the same time, on the basis of feature extraction, use the special cyclic neural network (RNN) in deep learning-long and short-term memory network (LSTM) to build a model of data for data prediction, under the powerful ability of LSTM to process temporal data, they have unearthed more irregular and non-linear trends in a large amount of historical data, and achieved better prediction results than traditional methods (ARIMA). Through clustering and prediction of lubrication parameter data, it can realize early warning and abnormal diagnosis of lubrication status and reduce damage to machinery and equipment.

https://iopscience.iop.org/article/10.1088/1755-1315/687/1/012029/pdf

Classification and prediction of wear state based on unsupervised learning of online monitoring data of lubricating oil

In order to solve the problem of continuity and accuracy of regional pedestrian identification, a method for regional identification and tracking of specific pedestrians with fusion of face information is proposed by using the front and rear frame information of the video stream. The FaceNet face identity network is constructed using the lightweight MobilenetV3-Large feature extraction network, and a fully connected layer is added for classification. Cross-Entropy Loss and Triplet Loss are integrated to build a classifier for stable loss convergence. The identity recognition method is introduced into DeepSORT, and an identity constraint module is added to solve the IDswitch caused by changes in appearance or pedestrian direction. This method combines the two modules of identification network and pedestrian tracking, and then realizes the continuous identification and tracking of pedestrians in the area. The experimental results show that: in terms of identity recognition, the accuracy of the improved system is 11% higher than that of the original algorithm in the autonomous test link; the accuracy of the tracking model is 69.S3% in MOTA on the MOT16 dataset, and the improved system is compared with the original algorithm in the autonomous test. During the test, MOTA increased from 72.38% to 89.84%, while IDswitch was greatly reduced. It realizes the detection and continuous tracking of specific pedestrians in the area, and provides technical support for the behavior of pedestrians in the area.

Improved DeepSORT-based pedestrian tracking and recognition method with fused face information

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