Maintenance Of Mechanical Equipment Research Articles

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.

Remaining useful life prediction of rolling bearing via composite multiscale permutation entropy and Elman neural network

Accurate prediction of remaining useful life (RUL) is very important for the maintenance of mechanical equipment. With the latest development in sensor technology and artificial intelligence, the data-driven prediction method of mechanical equipment RUL has been widely concerned. However, in the past, the research on data mining was not sufficient, and the method of extracting degradation features was single. In order to solve this problem, this paper proposes a new method to extract the degradation features of bearings. This method creatively combines image with entropy methods and uses the Symmetrized Dot Pattern (SDP) method and Composite Multiscale Permutation Entropy (CMPE) to extract degradation features. Through five evaluation indexes, the degradation feature extracted by the SDP-CMPE method is compared with five common degradation features in time domain. Finally, a simple Elman neural network is used to predict RUL. Through the case study of bearing data set, it is verified that the degradation features extracted by the SDP-CMPE method are superior in prediction accuracy and conservatism.

Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. This study aims to enhance the practicality of bearing fault diagnosis to meet real-world engineering requirements. In real industrial environments, the continuously changing operating conditions such as equipment speed and load pose challenges in collecting data for bearing fault diagnosis, as it is challenging to gather data for all operational conditions. This paper proposes a transfer learning approach for bearing fault diagnosis based on adaptive batch normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the Case Western Reserve University dataset and Northeast Forestry University (NEFU) dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.

Self-matching extraction fractional wavelet transform for mechanical equipment fault diagnosis

Time-frequency analysis (TFA) of vibration signals is an important way for fault diagnosis and predictive maintenance of mechanical equipment. However, the existing TFA methods, such as wavelet transform (WT) and synchrosqueezing transform, are incapable to describe local details of strongly time-varying signals, bringing such problems as time-frequency energy dispersion. Theoretically, it is more beneficial for time-varying band-limited signals to make multi-resolution analysis in the time-fraction frequency domain because such action can increase the selection chances of mother wavelets in any direction on the time-frequency plane by changing the angle . In order to improve the ability of analyzing strongly time-varying signals and the readability of the time-frequency plane, this paper has proposed a novel TFA method called self-matching extraction fractional WT (SMEFRWT). Firstly, within the theoretical framework of fractional WT (FRWT), a minimum entropy-based method is proposed to choose an optimal angle in the fractional domain. Such action can obtain the best aggregation angle of time-frequency energy and facilitate the separation of multi-component signals. Then, self-matching extraction operator, one instantaneous-frequency estimated operator that match the time-frequency structure of amplitude modulation signals, is constructed. The operator allows for the distribution of time-frequency energy of fast-changing signals in both frequency and time directions, making it possible to match the time-varying vibration signals from mechanical equipment more accurately. Finally, SMEFRWT is adopted to make TFA and extract fault features of the faulty experiment benches on gear and bearings, with its feasibility validated by experimental results.

ANALISIS REALISASI BIAYA PADA KEGIATAN OVERBURDEN REMOVAL DI PT ABC KABUPATEN LAHAT PROVINSI SUMATERA SELATAN

Mining sector is very closely related to mechanical device utilization and is inseparable from cost so that production targets can be achieved. Operational costs can affect the company’s income, so an analysis of operational costs is necessary. This study analyzed the realization of operational costs in fleet 1 overburden removal activities to determine lost opportunities experienced by a company. The analysis process was conductued by identifying the target and actual production of overburden fleet 1. The production target of overburden removal was 15,000 BCM with the tools used 1 digging equipment and 3 hauling equipment and the operational cost consisted of solar cost, oil cost, filter cost, grease cost, tire cost, repair/maintenance cost and operator salary cost. The total operational cost for overburden removal activity was Rp237,579,500 with the overburden transportation cost of Rp29,500 for a distance of 1.2 Km, the gross income carned was Rp435,309,080. The factors that caused the lost opportunity were the unachieved production factor of overburden removal fleet 1 which was Rp7,190,920 and the mechanical equipment maintenance factor which was Rp103,261,000 with a lost opportunity of Rp110,451,920.

Digital Twin-Driven Remaining Useful Life Prediction for Rolling Element Bearing

Traditional methods for predicting remaining useful life (RUL) ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of RUL and affecting the normal working of rolling element bearing (REB). To solve the above problem, we propose a hybrid method based on digital twin (DT) and long short-term memory (LSTM). The hybrid method combines the high simulation capabilities of DT and the strong data processing capabilities of LSTM. Firstly, we develop a DT system for the life characteristics analysis of an REB. When the DT system is implemented, we can obtain the theoretical value of RUL. Then, the experimental data is used to train the LSTM model. The output of LSTM is the actual value of RUL. Finally, the particle swarm optimization (PSO) algorithm fuses the theoretical values of DT with the actual values of LSTM. The case study demonstrates that the prediction accuracy of the hybrid method is greater than 97.5%, which improves the prediction performance and robustness of RUL. Therefore, the hybrid method is an important technology of REB prediction and health management (PHM). It realizes the early intervention and maintenance of mechanical equipment and ensures the safety of enterprises’ production.

Gearbox compound fault diagnosis method based on deep adversarial graph convolution transfer learning network under low label ratios

Advancement in measurement techniques has dramatically contributed to the development of the modern manufacturing industry. As the primary fault causing unplanned downtime of mechanical equipment, gearbox compound faults are usually coupled by single faults with unequal severity and are difficult to obtain. In industrial scenarios, monitoring data for extreme operating conditions is not available in advance, and labeling samples is time-consuming and costly. However, current research on unseen compound fault diagnosis relies on numerous labeled samples to train the model, and few studies are conducted on transfer learning and unseen compound fault diagnosis under low label ratios. To address the issue, a gearbox compound fault diagnosis method based on deep adversarial graph convolution transfer learning network (DAGCTLN) under low label ratios is proposed. Specifically, a novel DAGCTLN model, including a feature extractor, two label classifiers, and a discriminator, is constructed to realize the diagnosis of faults in the transfer domain and unseen compound faults in the source and target domains. The feature extractor of a three-layer graph convolutional network is presented to achieve deep extraction of fault features under low label ratios. Then a domain space adversarial mechanism between the feature extractor and discriminator is used to achieve global alignment of the source and target domain features. Furthermore, two label classifiers are constructed, and the adversarial adaptation of the decision boundary is realized by maxi-min the classifier difference to achieve subdomain alignment of the same class features in all domains. Experimental results indicate that DAGCTLN can achieve the highest fault diagnosis accuracy in the transfer domain compared to state-of-the-art algorithms. The average diagnosis accuracy of compound faults in all domains can reach 98.41% even if the label ratio is only 0.1, which provides guiding significance for the safe operation and predictive maintenance of mechanical equipment.

Analytical conclusions and proposals for technical condition and effective use of facilities on Tashkent magistral canal

The socio-economic development of the Tashkent region (Uzbekistan) depends largely on natural resources, especially water resources, as in other regions. One of the canals that supply water to the Tashkent province is the Tashkent magistral canal (TMC). In terms of size, the second of the canals receives water from the left bank of the Chirchik River. The TMC has been 82 years since its start of use. Hydrotechnical facilities on the TMC are typical of the II class. There are many hydrotechnical facilities on the canal: one main structure, four water dampening facilities, two destructive water discharges, four duckers in the canal, 45 water dischargers, one waterfall, seven under-canal duckers, two pipes under the canal, nine aqueducts, nine canal access, thirteen road bridges, one railway bridge, one pedestrian bridge. This article presents the technical condition of the TMC and its hydrotechnical facilities and recommendations developed based on the technical condition. On April 9-13, 2022, field observation work was carried out to study the technical condition of the TMC and hydrotechnical facilities. It reviewed the availability of technical documents of TMC facilities, instructions for their use, information on maintenance of mechanical equipment, working drawings, previous inspection reports, and other materials. In addition, the conclusions of the inspection carried out by the State Inspection "Davsuvkhujalikzorat" and the results of the technical inspections carried out by the employees of the TMC administration and project parameters of water facilities were studied.

Fuzzy Broad Learning System Combined with Feature-Engineering-Based Fault Diagnosis for Bearings

Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis.

Remaining useful life prediction of bearings by a new reinforced memory GRU network

The remaining useful life (RUL) prediction of bearings has great significance in the predictive maintenance of mechanical equipment. Owing to the difficulty of collecting abundant lifecycle datasets with correct labels, it is quite necessary to explore a prediction method with high precision and robustness in the case of small samples. It follows that a novel RUL prediction approach is put forward to overcome this problem. First, for reducing the man-made interference and the demand for expert knowledge, an unsupervised health indicator (HI) is constructed by Gaussian mixture model (GMM) and Kullback-Leibler divergence (KLD), which is named as KLD-based HI. Then because of the rapid forgetting of historical trend information in the current RNN-based prediction models, a novel reinforced memory gated recurrent unit (RMGRU) network is proposed by reusing the state information at the previous moment. According to the constructed KLD-based HI vector, the unknown HIs are successively predicted by RMGRU until the predicted HI value exceeds the failure threshold, and then RUL is calculated. The contrast experiment on IEEE 2012PHM bearing datasets shows the superiority of the bearing RUL prediction approach based on RMGRU over the classical time series forecasting methods. It can be concluded that this method has great application potential in bearing RUL prediction.

Measures for Installation, Construction and Maintenance of Mechanical Equipment in Metallurgical Engineering

Measures for Installation, Construction and Maintenance of Mechanical Equipment in Metallurgical Engineering

Comprehensive detection method for multi-contaminants in hydraulic oil based on inductance-resistance-capacitance analysis

Oil condition monitoring technology is a practical method for mechanical fault diagnosis, because oil contamination can directly reflect the lubrication and wear state. A comprehensive detection method based on integrated sensor consisting of an inductance-resistance sensing unit and a capacitance sensing unit is introduced in this article. Inductance parameter differentiates metallic debris, and the coil-based resistance parameter can detect smaller non-ferromagnetic metallic debris. Capacitance parameter is used to measure air bubbles and moisture, and the moisture measurement is not interfered by solid impurities. The simulation analysis showed that annular channel reduces the instability of inductive-resistive detection caused by position effect. The experimental results proved that the integrated inductive-resistive-capacitive sensor detects air bubbles, moisture, ferromagnetic debris and non-ferromagnetic debris by comparing multi-parameter signals. This method expands the detection range for oil contamination, and reduces the probability of incorrect particle counting and incorrect size measurement, which provides technical support for the intelligent operation and maintenance of mechanical equipment.

Discussion on Installation, Construction and Maintenance of Mechanical Equipment in Metallurgical Engineering

Discussion on Installation, Construction and Maintenance of Mechanical Equipment in Metallurgical Engineering

A Dual-Guided Adaptive Decomposition Method of Fault Information and Fault Sensitivity for Multi-Component Fault Diagnosis Under Varying Speeds

Fault diagnosis is essential for the safe operation and subsequent maintenance of mechanical equipment. However, multifault mutual interference brings great challenges to fault diagnosis when multicomponent faults occur simultaneously. Furthermore, since multiple components operate under different varying-speed conditions, it is difficult to match fault information from different components to their respective speeds, making fault diagnosis even more difficult. A dual-guided adaptive decomposition method of fault information and fault sensitivity (FIFS-ADM) is proposed to address the above issues. First, a fault information-driven empirical wavelet transform whose segmentation boundaries are optimized by fault richness index is presented to separate multicomponent faults, thereby eliminating multifault interference. Then, multiorder tracking that refers to using multiple speed signals to resample the decomposed subsignals, respectively, is performed to convert these speed-varying subsignals into angular-domain stationary signals. Finally, a health recognition matrix composed of multicomponent fault sensitivity indicators is introduced to achieve not only the matching of fault component and speed but also fault location and health assessment of multiple components. Furthermore, simulated signals and actual signals are built to validate the proposed method, which shows the effectiveness and superiority of FIFS-ADM.

Fault Detection and Maintenance of Hydraulic Power Generation System Based on Computer Operated Machinery and Equipment

With the development of information technology, the fault detection and state maintenance of power generation system in hydraulic power generation industry pay more and more attention to information operation. In this paper, the necessity of mechanical equipment maintenance of hydraulic power generation system and the related matters to pay attention to are explained, at the same time, the fault detection and maintenance level of hydraulic power generation system based on computer operation of mechanical equipment is discussed, for the reference of readers.

Evaluation of risk for construction safety of fabricated buildings based on analytic hierarchy process

In order to assist the construction safety risk control of fabricated building, 17 safety risk factors were identified from the five dimensions of man, machine, material, management, and environment. The safety risk evaluation index system of the fabricated building construction stage is constructed based on the AHP (Analytic Hierarchy Process). The risk level is divided according to weight. The results show that the focus should be on the on-site safety staffing, the professional ability of the operators, the quality of additional tools, and the overhaul and maintenance of mechanical equipment. Enough attention should be paid to material storage and maintenance, operator safety awareness, deployment of safety protection measures, transportation protection measures, operator protection work, mechanical equipment selection, natural environment, safety inspection work plan, prefabricated component production quality, mechanical equipment installation, and removal. General attention should be paid to the surrounding environment, the safety production responsibility system, and the construction site environment.

How to Do Well the Maintenance of Mechanical Equipment

How to Do Well the Maintenance of Mechanical Equipment

RETRACTED ARTICLE: Enterprise technology innovation and production performance based on machine learning and IoT artificial intelligence system

Repairing industrial equipment in accordance with the traditional maintenance model not only requires more maintenance expenses, but also sometimes causes irreversible damage to the equipment due to the inability to repair the equipment in time. The application of artificial intelligence technology to the maintenance mode of industrial equipment can not only reduce maintenance costs to a great extent, but also repair the equipment in time when the equipment fails. Because the theory of applying artificial intelligence technology to the maintenance of mechanical equipment was proposed late, the application of artificial intelligence technology in the field of mechanical maintenance at home and abroad is not extensive. Because of this, the research on many intelligent models is still in the theoretical stage, and because of the few examples, the correctness of many theories is still difficult to determine. In this context, based on the application status of intelligent maintenance mode and industrial Internet of things, this article takes the most widely used rolling bearing component design as the research object, combines industrial data collection technology and computer learning fault diagnosis algorithm, and proposes a new type of intelligent equipment fault Diagnosis system. My country’s industry has achieved outstanding results since the founding of the country, and every industry that has developed too rapidly is facing the problems of excessive industrial factors, difficulties in industrial transformation, and stagnant industrial development. Therefore, if we want to ensure that my country’s industry maintains a good state of development for a long time, we must change the industrial structure of my country's industry, optimize the industrial production model, reduce the input of industrial factors, and carry out economic transformation.

Research on the Effectiveness of Hybrid Teaching of General Mechanical Equipment Maintenance Course

Research on the Effectiveness of Hybrid Teaching of General Mechanical Equipment Maintenance Course

MOOC Construction of General Machinery Equipment Maintenance

This paper took the MOOC construction of general mechanical equipment maintenance as an example to study its construction. Aspects on course construction foundation, construction objectives, knowledge reconstruction, construction content, implementation effect, existing problems and improvement measures were studied so as to provide reference for the construction of MOOC in higher vocational colleges.