Mechanical Equipment Research Articles

A hybrid physics-corrected neural network for RUL prognosis under random missing data

In order to accurately predict the remaining useful life (RUL) of mechanical equipment under conditions of missing data, this paper proposes a novel integrated framework for data repair and RUL prediction, named DRTCR. DRTCR possesses the dual functionality of missing data repair and RUL prediction. Initially, considering the underlying trends and periodic fluctuations in the signals, polynomial and trigonometric functions are incorporated into the implicit feature matrix (IFM) generated by Gaussian random functions. This approach involves constructing a data repair deep learning framework primarily based on LSTM and CNN. This framework facilitates the repair of missing data. Furthermore, this paper introduces the integration of trend, correlation, and robustness within the LSTM units. A predictive unit named TCRcell is devised, which enhances the feature extraction capability of the prediction model. Additionally, a corrective unit within TCRcell is constructed, predominantly centered around discrete Fourier transform (DFT) and discrete wavelet transform (DWT) as frequency domain feature extraction techniques. This incorporation introduces the physical mechanism information into DRTCR and refines the predictive outcomes of TCRcell. The global frequency perspective of DFT aids in capturing the overall trend in signal variations, while DWT precisely captures localized temporal details in the time series, resulting in a dynamically corrected TCRcell prediction outcome that combines both global trends and local intricacies. Finally, experimental validation was conducted to assess the predictive performance and robustness of DRTCR.

DecouplingNet: A Stable Knowledge Distillation Decoupling Net for Fault Detection of Rotating Machines Under Varying Speeds.

Fault detection, also known as anomaly detection (AD), is at the heart of prediction and health management (PHM), which plays a vital role in ensuring the safe operation of mechanical equipment. Nonetheless, the lack of anomaly data creates a significant obstacle to the AD of the mechanical system. In particular, the complex modulation effects induced by time-varying speeds make AD much more challenging. For rapid and accurate AD, a stable knowledge distillation decoupling net (DecouplingNet) is provided to overcome these difficulties. First, an adversarial network consisting of an encoder, a decoder, and an encoder-discriminator is developed to model normal samples well by imposing constraints on the latent space. Then, a causal decoupling framework is suggested to disentangle equipment state-related information from operating conditions-related features, enabling stable condition monitoring at varying speeds. Finally, feature-based knowledge distillation is employed to boost the efficiency of AD while maintaining the detection accuracy. The proposed method is tested on two experimental scenarios and compared with some typical AD methods. The finding demonstrates that the net outperforms others in terms of accuracy and efficiency when it comes to detecting anomalies in the mechanical equipment that runs under varying speeds.

Mechanical simulation analysis of an electric scroll compressor

Abstract As a precision automotive component, electric scroll compressors have high requirements for reliability. In order to save the cost of environmental testing and shorten the market cycle of new products, the modal parameters of a compressor are measured, and the first six modal parameters are obtained. Secondly, the simplified finite element model of the compressor is established, modal analysis is carried out, and the modal frequency error obtained by the two methods is controlled within a reasonable range to ensure the accuracy of the modeling. Finally, based on the established simulation model, the structural response and stress of the compressor under various working conditions, such as sinusoidal vibration and random vibration, are analyzed, and the extreme values of displacement and stress are obtained. The results show that the structural design of the compressor meets the requirements of the environmental test of on-board equipment. The test and simulation methods used in this paper also have certain reference significance for other mechanical equipment.

A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms

A Fault Monitoring System for Mechanical and Electrical Equipment of Subway Vehicles Based on Big Data Algorithms

Research on rolling bearing fault diagnosis technology based on singular value decomposition

To solve the difficulty of selecting the number of effective singular values in Singular Value Decomposition denoising, a new method to determine the number of effective singular values is proposed. The proposed method to determine the number of effective singular values is based on the non-zero singular value distribution law of the Hankel matrix constructed by the signal. Specifically, the number of effective singular values in the Hankel matrix is twice the number of frequencies contained in the signal, and the difference between the effective singular values of the noisy signal and the non-zero singular values of the pure signal is very small. The proposed method for determining the number of effective singular values is to perform differential processing on the singular values of the signal and normalize the difference obtained. An empirical parameter T is provided, and the number of effective singular values is determined by comparing them with the normalized results. The proposed method is applied to the simulated and measured rolling bearing signals, and the results are compared with the wavelet threshold denoising method. The results show that the proposed method for determining the number of singular values can effectively filter out the noise frequency contained in the signal while maintaining the characteristic frequency of the signal and achieving the purpose of mechanical equipment fault diagnosis.

Adaptive Convergent Visibility Graph Network: An interpretable method for intelligent rolling bearing diagnosis

In the domain of mechanical equipment maintenance, the necessity for efficient and accurate fault diagnosis is critical. Traditional Graph Neural Network (GNN) methods, which employ time-series data for fault diagnosis, have proven effective but are far from perfect. Their common pitfall lies in mapping time-series data into graph data, often leading to loss of crucial temporal information and computational inefficiencies. These limitations could result in suboptimal diagnosis, potentially compromising the longevity and performance of mechanical systems. Driven by the need to improve on these limitations, we introduce ACVGN, a novel end-to-end intelligent diagnostic framework that unites the strengths of the Adaptive Convergent Visibility Graph (ACVG) algorithm and an enhanced DiffPool model. The cornerstone of our approach, the ACVG algorithm, adeptly transforms time-series data into graph format, thereby preserving both local and global dynamics from the original data. This rich representation is then processed by our improved DiffPool model, a powerful GNN model purposefully designed for high-accuracy classification tasks. The effectiveness of the proposed ACVGN framework is substantiated by its performance on the widely-used rolling bearing dataset, where it outshines existing methods in terms of both mapping efficiency and fault diagnosis accuracy. These promising results not only reinforce the effectiveness of our proposed method but also highlight the potential for its wider applicability in other scenarios involving time-series data analysis and graph-based machine learning tasks. In conclusion, our study advances the development of more intelligent, efficient, and precise diagnostic tools for mechanical devices, ensuring more effective fault detection and diagnosis, and thereby potentially improving the lifespan and functionality of mechanical systems.

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.

Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

In an actual industrial environment, the complex working environment of mechanical equipment may lead to new faults in the target domain, called the open-set domain adaptation problem. Recently, open-set adaptive fault diagnosis has been extensively employed. However, most studies not only require pre-set fixed thresholds to identify unknown class features but also ignore the learning of discriminable features under specific tasks, which affects the diagnostic performance. Hence, this paper proposes a complementary weighted dual adversarial network combined with supervised contrastive learning (CWDAN-SCL) to address the open-set cross-different working fault diagnosis of bearings. Specifically, a novel complementary weighted adversarial learning strategy is designed using supervised classification and uncertainty measurement to effectively control the participation of target domain features in the domain adaptation process and achieve the alignment of shared class fault features between the source and target domains. Moreover, an adaptive unknown fault separation module is designed using an adversarial learning method to construct a hyperplane between shared and unknown class fault features in the target domain to identify unknown class faults accurately. Additionally, a supervised contrastive loss term is designed based on contrastive learning and label knowledge to improve the aggregation of fault features of the same class and enhance the model’s generalization ability in target domain diagnosis tasks. Subsequently, the efficacy and advancement of the proposed method are substantiated through experimentation on two datasets. The experimental results illustrate that the average diagnostic performance of the proposed method is 91.73 %. This study contributes a dependable diagnostic approach for ascertaining the health status of rotating machinery equipment.

GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis.

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studied. However, the imbalance in failure data and the complexity of time series data make diagnosis challenging. Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM), and extreme gradient boosting (XGBoost)) face limitations in diagnosing such failures. To address this problem, this paper proposes a bearing failure diagnosis model using a graph convolution network (GCN)-based LSTM autoencoder with self-attention. The model was trained on data extracted from the Case Western Reserve University (CWRU) dataset and a fault simulator testbed. The proposed model achieved 97.3% accuracy on the CWRU dataset and 99.9% accuracy on the fault simulator dataset.

Parallel quantized dual-level fully connected classifier for bearing fault diagnosis

The diagnosis of bearing failures in rotating mechanical equipment is critical to productivity and the quality of the manufacturing process. Traditional methods generally suffer from high human factor interference and low accuracy. This paper proposes a parallel quantized dual-level fully connected classifier (PQDFCC) for fault identification of bearings. The PQDFCC has two parts. The first part of the PQDFCC contains two types of data pre-processing. The first data pre-processing method is calculating absolute values and sorting the acquired raw signals. The second method of data pre-processing is quantizing the raw data. The second part of PQDFCC is a dual-level fully connected classifier. The first-level fully connected classifier performs the first-level fault classification on the quantized and sorted data; then, the output probability of the first-level classifier is considered as the input of the second-level fully connected classifier; finally, the second-level fully connected classifier provides the classification results of bearing faults. The data pre-processing part of the PQDFCC is a simple calculation that can simplify and not lose fault information; the dual-level fully connected classifier can fully exploit the hidden features of the data with high accuracy. The proposed PQDFCC is tested in the bearing failure, axial piston hydraulic pump, and self-priming centrifugal pump datasets; the experimental results show that the PQDFCC achieves 100% fault diagnosis accuracy.

Penguatan Manajemen pada Usaha Kerajinan Bambu Jihan Craft Desa Somakaton

Partners in this program are bamboo craftsmen Jihan Craft, Somakaton Village, Somagede District, Banyumas Regency. The problems faced by partners are lack of equipment in running their business; very limited capabilities in business management consisting of production management, HR, marketing and finance; limited ability in bookkeeping and financial reporting; limited ability to carry out online marketing activities; do not form groups; do not understand group management; and do not have the skills to carry out group administration. To achieve the goals, activities are carried out in the form of counseling, practice and mentoring. The evaluation method is carried out by comparing the level of knowledge and abilities as well as the production and marketing performance of partners before and after the activity. The benefits of this service activity are that partners get additional mechanical production equipment so they can increase the quantity and quality of their products and can produce more efficiently, partners' business management abilities increase, they have the ability to do bookkeeping and financial reporting, they can carry out online marketing activities well, the formation of groups Jihan Craft business officially, understand and can practice group management well; and can carry out group administration activities well.

Molecular Dynamics Simulation and Experimental Study of Friction and Wear Characteristics of Carbon Nanotube-Reinforced Nitrile Butadiene Rubber

Nitrile butadiene rubber (NBR) and its various composite materials are widely employed as friction materials in mechanical equipment. The use of carbon nanotube (CNT) reinforcement in NBR for improved friction and wear characteristics has become a major research focus. However, the mechanisms underlying the improvement in the friction and wear characteristics of NBR with different CNT contents remain insufficiently elucidated. Therefore, we conducted a combined analysis of NBR reinforced with varying CNT contents through molecular dynamics (MD) simulations and ring–block friction experiments. The aim is to analyze the extent to which CNTs enhance the water-lubricated friction and dry wear properties of NBR and explore the improvement mechanisms through molecular chain characteristics. The results of this study demonstrate that as the mass fraction of CNTs (0%, 1.25%, 2.5%, 5%) increases, the water-lubricated friction coefficient of NBR continuously decreases. Under water-lubricated conditions, CNTs improve the water storage capacity of the NBR surface and enhance lubrication efficiency. In the dry wear state, CNTs help reduce scratch depth and dry wear volume.

Gearbox Fault Diagnosis Based on MSCNN-LSTM-CBAM-SE.

Ensuring the safety of mechanical equipment, gearbox fault diagnosis is crucial for the stable operation of the whole system. However, existing diagnostic methods still have limitations, such as the analysis of single-scale features and insufficient recognition of global temporal dependencies. To address these issues, this article proposes a new method for gearbox fault diagnosis based on MSCNN-LSTM-CBAM-SE. The output of the CBAM-SE module is deeply integrated with the multi-scale features from MSCNN and the temporal features from LSTM, constructing a comprehensive feature representation that provides richer and more precise information for fault diagnosis. The effectiveness of this method has been validated with two sets of gearbox datasets and through ablation studies on this model. Experimental results show that the proposed model achieves excellent performance in terms of accuracy and F1 score, among other metrics. Finally, a comparison with other relevant fault diagnosis methods further verifies the advantages of the proposed model. This research offers a new solution for accurate fault diagnosis of gearboxes.

Design of PID control model for reducer based on Simulink simulation

At present, modern industry is developing rapidly, and the level of industrial automation is gradually improving. Many production lines use a large number of mechanical equipment and robots, and reducers play a key role in these equipment, helping to control and adjust the speed and torque of various moving parts, achieving efficient production. At the same time, in high-speed railway trains and airplanes, reducers improve the efficiency of engines and turbines, reduce fuel consumption, and ensure safe and reliable operation. However, traditional reducers have drawbacks such as low accuracy and long adjustment time in controlling speed. This article adopts the method of PID control of the reducer, and uses the Simulink platform of Matlab to build a control model of the reducer. At the same time, negative feedback control is applied to the speed and current, forming two PID closed-loop controls: current loop and speed loop. The speed waveform and current waveform are displayed on an oscilloscope, and the simulation control results are analyzed. The simulation results show that by using a PID controller to control the speed of the reducer, the maximum overshoot can be reduced, the adjustment time can be shortened, the adjustment process can be smoother, and the set value can be reached quickly.

Digital transformation as a factor in reducing labor shortages in Russian agriculture

This article examines the dynamics of the number of employees of agricultural organizations, the dynamics of energy availability, electrical equipment, the level of mechanization and technical equipment as key conditions for the introduction of digital solutions and the reduction of labor shortages in the industry. In addition, an analysis of the dynamics of labor costs in the sectoral and product context was carried out, which allows us to identify the most labor-intensive and requiring the introduction of digital solutions in the agricultural sub-sector. The key digital technologies that contribute to reducing the shortage of labor resources have been studied. A factorial regression model is proposed that allows us to study the impact of digital transformation on reducing the labor costs of producing a unit of production. The results of the study allowed us to propose an approach to assessing the impact of the digital transformation of agriculture on labor costs and reducing the shortage of labor resources of an industry or an individual enterprise (group of enterprises), consisting in analyzing the dynamics of the need for workers, the conditions of technical equipment of production, labor costs and approximation based on a regression model of the impact of digital transformation technologies on labor cost reduction.

Mitigating vibration from building systems in a second-floor laboratory

A second-floor laboratory reported excessive vibrations that were impacting the calibration of microscopes, as well as human comfort. Based on a review of the building floor plans and discussions with the Design-Build team, potential vibration sources were narrowed to various MEP equipment in the first-floor mechanical room, rooftop, and outdoor utility pad. Vibration measurements were collected in the laboratory and near each piece of mechanical equipment as the equipment was turned ON and OFF to determine its vibration impact on the laboratory. The narrowband frequency data indicated that the rotational frequency of the ceiling-mounted hot water pumps coincided with the frequencies of excessive vibration in the lab. Vibration isolation for the hot water pumps and surrounding piping and equipment was reviewed, and recommendations were provided based on ASHRAE guidance to minimize vibration propagation to the laboratory.

Considerations for extending sound studies beyond typical regulatory compliance requirements for data centers in suburban and rural environments.

The escalating presence of data centers in suburban and rural environments has emerged as a potential new source of noise in areas often lacking comprehensive noise regulations. Where noise regulations do exist, many do not provide spectral limitations or other methods of assessing and regulating the frequently cited community concerns such as low-frequency noise (LFN) and tonality of the sounds. This may lead to public dissatisfaction of the data center sounds despite compliance with the applicable noise limits. This paper provides an overview of representative codes and standards to help illustrate the deficiencies in some codes to identify community concerns. In addition, the paper conducts an initial examination of sound spectra associated with a variety of mechanical equipment types used for cooling data halls in these facilities, focusing on identifying outliers or common characteristics that may cause community annoyance. This review emphasizes the need for heightened scrutiny during the due diligence or design phase of data center projects.

A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis

Graph convolutional networks (GCNs) as the emerging neural networks have shown great success in Prognostics and Health Management because they can not only extract node features but can also mine relationship between nodes in the graph data. However, the most existing GCNs-based methods are still limited by graph quality, variable working conditions, and limited data, making them difficult to obtain remarkable performance. Therefore, it is proposed in this paper a two stage importance-aware subgraph convolutional network based on multi-source sensors named I2SGCN to address the above-mentioned limitations. In the real-world scenarios, it is found that the diagnostic performance of the most existing GCNs is commonly bounded by the graph quality because it is hard to get high quality through a single sensor. Therefore, we leveraged multi-source sensors to construct graphs that contain more fault-based information of mechanical equipment. Then, we discovered that unsupervised domain adaptation (UDA) methods only use single stage to achieve cross-domain fault diagnosis and ignore more refined feature extraction, which can make the representations contained in the features inadequate. Hence, it is proposed the two-stage fault diagnosis in the whole framework to achieve UDA. In the first stage, the multiple-instance learning is adopted to obtain the importance factor of each sensor towards preliminary fault diagnosis. In the second stage, it is proposed I2SGCN to achieve refined cross-domain fault diagnosis. Moreover, we observed that deficient and limited data may cause label bias and biased training, leading to reduced generalization capacity of the proposed method. Therefore, we constructed the feature-based graph and importance-based graph to jointly mine more effective relationship and then presented a subgraph learning strategy, which not only enriches sufficient and complementary features but also regularizes the training. Comprehensive experiments conducted on four case studies demonstrate the effectiveness and superiority of the proposed method for cross-domain fault diagnosis, which outperforms the state-of-the art methods.

Remaining useful life prediction of mechanical equipment based on time-series auto-correlation decomposition and CNN

Remaining useful life prediction of mechanical equipment based on time-series auto-correlation decomposition and CNN