Mechanical Equipment Research Articles

Bearing fault diagnosis based on enhanced Canberra distance feature in SDP image

Abstract Feature enhancement is important in mechanical equipment fault diagnosis. A limited set of characteristic parameters is insufficient for diagnosing bearing signals with multiple fault types. The presence of noise increases the difficulty of extracting fault features from images. To address the challenge of diagnosing rolling bearing faults in complex environments, this study presents an enhanced weighted image fusion framework aimed at enhancing fault features within the images, which enables accurate diagnosis of bearing faults using a limited number of features. The proposed method encompasses four distinct stages. In the first stage, a symmetrized dot pattern method is employed to transform one-dimensional time-series data into two-dimensional images, visualizing the signal in a 2D format. In the second stage, image binarization and an improved weighted fusion method are utilized to simplify subsequent processing and enhance the image features. The third stage involves extracting the image’s contrast and maximum singular value to improve the Canberra distance calculation. Finally, the enhanced Canberra distance is used for classifying bearing faults. Performance testing of the image feature enhancement is conducted on various datasets containing rolling bearings. Comparative experiments with alternative enhancement methods demonstrate the superiority of the proposed improved weighted image fusion framework. Comparative experiments with the original Canberra distance validate the effectiveness of the enhanced Canberra distance. Additionally, experiments conducted in noisy environments confirm the robustness of the proposed approach. Furthermore, the image feature enhancement method is applied to other bearing datasets, and the experimental results demonstrate its effectiveness in enhancing fault feature representation and achieving accurate diagnosis of rolling bearings.

A bearing fault data augmentation method based on hybrid-diversity loss diffusion model and parameter transfer

The fault diagnosis of mechanical equipment can prevent potential mechanical failures, avoid property damage and personal injury, and ensure the stable and safe operation of mechanical equipment. Data driven is an important aspect of intelligent fault diagnosis. When data is scarce, it can seriously affect the accuracy of fault diagnosis and make it difficult to ensure the smooth and safe operation of machinery. Faced with this challenge, a classifier-free guidance diffusion model combining hybrid loss and diversity loss (CFGDMHD) is proposed for data augmentation of fault samples. This new data augmentation method generates samples with the same data distribution as real samples from random noise through diffusion process. CFGDMHD can generate multi-class samples simultaneously without the need for additional classifier guidance in the joint training of unconditional diffusion models and conditional diffusion models. This work proposes diversity loss to improve the diversity of generated samples. We conducted experiments using a bearing dataset. The results indicate that the sample quality and diversity generated by this method are excellent, which can help improve the accuracy of fault diagnosis and ensure the safe operation of mechanical systems.

Adaptive feature mode decomposition method for bearing fault diagnosis under strong noise

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Mechanical noise for architectural students

Noise control of mechanical equipment is one of the requirements established in energy certificates (LEED, BREEAM, WELL, etc.) as well as in different design standards or national codes. At the Higher Technical School of Architecture of Madrid, Technical University of Madrid (UPM), several courses are offered to students on the design of mechanical systems, from the selection of the equipment, its location, distribution, and its integration with the architecture. Its acoustic implications are not explained in any course. During the last year of the degree, in the course "Proyecto de instalaciones", (Technical Equipment Design) students are asked to fully integrate the building services (heating, cooling, ventilation, fire safety, emergency signals...) in one building designed by their own, and for the first time a 2-hour masterclass on mechanical noise for architects has been organized. In it, the main noise sources, design recommendations and the most common ways to attenuate and control it are addressed. The aim of this work is to share the learned experiences and discuss the next steps to be taken.

Seismic behaviour and design of a tall mixed reinforced concrete–steel structure supporting an oil refinery reactor

AbstractThis study investigates the seismic behaviour of a special mixed reinforced concrete-steel structure that supports an oil refinery reactor. The structure is 64.90 m tall and consists of three parts: (a) a reinforced concrete frame basement; (b) a steel braced frame that supports the oil reactor and (c) the steel reactor itself. A three-dimensional model of the structure is created to perform static non-linear (pushover) analyses in order to obtain the capacity curves and understand the overall inelastic behavior of the structure. The results of the pushover analyses reveal that the structure exhibits similar inelastic behavior in both horizontal directions and satisfies the capacity design principles. The structure exhibits limited ductility considering the fact that has been designed with a behavior factor of q = 1.5 and primary damages are expected mainly in concrete members. Subsequently, dynamic non-linear time-history (NLTH) analyses are performed utilizing the three translational components of three seismic motions recorded during past earthquakes. These results involve: (i) the maximum values for displacements, accelerations and base shears; (ii) the maximum stresses at critical points of the oil refining reactor and (iii) the formation of plastic hinges at columns, beams and braces of the structure. Contrary to pushover analyses, NLTH analyses revealed the development of plastic hinges, hence seismic damage, that do not follow the desirable formation pattern. Moreover, the accelerations and displacements observed are expected to cause failure of the piping and mechanical equipment, while local failure of the high-stress areas of the shell of the reactor may be possible. Localized strengthening might be necessary to avoid repair works and downtime after such seismic event.

Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network.

Coupling faults that simultaneously occur during the operation of mechanical equipment are widespread. These faults encompass a diverse range of high-order coupling relationships, involving multiple base fault types. Based on the advantages of hypergraphs for higher-order relationship descriptions, two coupling fault diagnosis architectures based on the hypergraph neural network are proposed in this paper: 1. In the coupling fault diagnosis framework based on feature generation, the base faults serve as the hypergraph nodes, and each hyperedge connects the base faults. The generator, which consists of the hypergraph neural network, generates coupling faults as negative samples to enforce regularization constraints for the discriminator training. 2. In the coupling fault diagnosis framework based on feature extraction, each node represents a fault mode, and each hyperedge connects nodes with common failure modes. The multi-head attention mechanism extracts the features of base faults, and the common fault features in a hyperedge are aggregated via the hypergraph neural network. The inner product correlation is used to diagnose the fault modes. The results show that the diagnostic accuracy for coupling faults with the two frameworks reaches 88.6% and 86.76%, respectively. Both frameworks can be used for the diagnosis and analysis of high-order coupling faults.

Computer Simulation and Simulation of Mechanical and Electrical Equipment based on Artificial Intelligence Algorithms

The computer in mechanical and electrical equipment can now detect equipment faults through simulation thanks to the advancement of artificial intelligence (AI) technology, which makes it convenient to monitor mechanical and electrical equipment. This paper begins with a quick introduction to Agent and the Agent system, explains how Agent is applied in the current context, then analyzes the hierarchical fault diagnostic model, identifies its flaws, and suggests improvement techniques. To increase the model’s accuracy and speed of operation, the contract net model and D-S (Dempster-Shafer) evidence theory are then incorporated. Finally, simulation experiments are used to confirm the accuracy and consistency of this model. According to the experimental findings, this optimization model runs at a pace that is noticeably faster than that of other models when subjected to the same workload, demonstrating the model’s efficacy. Models 1, 2, and 3 are put side by side to demonstrate how clearly multi-task processing may cut down on the model’s running time. In the second experiment, samples of mechanical and electrical equipment defect data are taken from two groups. The results of the comparative experiments demonstrate that the optimized model in this work can be reliable up to a maximum of 0.91 and a minimum of 0.63. It is demonstrated that the model in this work is rational by the fact that among the four types of fault prediction, the optimized model’s reliability is significantly greater than the traditional model’s. The research described in this publication therefore has some reference value for computer simulation of electromechanical equipment.

The experimental research of which different types of impact signals affect the bifurcation chaos behavior of three-degree-of-freedom collision system

Abstract Collision is a faulty behavior in mechanical equipment, which greatly affects the safety of equipment. In this paper, aiming at the collision failures that are prone to occur in mechanical equipment, the nonlinear fault collision behavior of a class of three-degree-of-freedom collision systems is studied. A three-degree-of-freedom piecewise non-smooth collision experiment platform system has been established to facilitate further analysis of the bifurcations and chaos inherent to the bearings system. This analysis is conducted through the use of Poincaré mapping on the collision plane. The excitation signals employed, which include sinusoidal, triangular, and rectangular signals, result in the emergence of specific types and orders of bifurcation behaviours within the system, as evidenced by the establishment of a Poincaré mapping at the collision surface, and this is observed to occur as the vibration frequency increases. The characteristics of impact signals generated by force sensors and the influence of impact signal type on the system are investigated. The nonlinear impact behavior of three-degree-of-freedom collision systems provides a reliable foundation for the design, fault diagnosis, and theoretical guidance of large-scale high-speed rotating machinery, as well as technical support for safe and stable operation of high-speed rotating machinery, which is essential for failure analysis and vibration reduction in equipment.

Development and Outlook for Self-priming Pumps

Background: Self-priming pumps are a type of general mechanical equipment that is widely used in the petroleum, chemical, electric power, metallurgy, mining, shipbuilding, light industry, agriculture, civil defense, and other departments, and they play an important role in the global economy. The pump system is the most common piece of equipment that consumes the most electricity, and reducing the energy consumption of the pump will save a significant amount of energy. Objective: For the benefit of scholars and engineers, this paper summarizes the most recent research on self-priming pumps, analyzes their solutions to the problem of reducing energy consumption, and discusses their advantages and disadvantages. Methods: The typical self-priming pump patents are outlined below based on the many functions of the improved self-priming pump structure, including pump body protection, high efficiency, and energy savings, ease of movement, and improved seal. The current self-priming pump patents are used to examine the development trend for these pumps. Results: The fundamental issues with self-priming pumps are outlined and studied through comparisons of various types and functions, and a future course for their development is suggested. Conclusion: The development of self-priming pump technology benefits from the improvement of self-priming pump structure. A self-priming pump with a straightforward design, automation, high efficiency, and energy conservation has a promising future.

Effect of radiation on lubrication performance and the consequences for mechanical equipment at CERN

Successful operation of the accelerator complex at CERN relies on Beam Intercepting Devices (BIDs) which are exposed to high doses of radiation and severely restricted maintenance access over their operating life. Instances of lubrication problems have been observed, such as high friction, overheating, corrosion and, in extreme cases, seizure. Understanding the root causes and the mechanisms controlling such failures is key to avoid downtime and improve maintenance schedules and processes. The paper reviews radiation degradation of lubricants and examines their tribological implications. Several examples of lubrication problems observed at CERN are investigated, and preliminary explanations are provided in the light of tests performed on irradiated samples. This highlights the need for better testing protocols and a thorough assessment of the time-varying performance of lubricants operating in the presence of radiation.

Label-guided contrastive learning with weighted pseudo-labeling: A novel mechanical fault diagnosis method with insufficient annotated data

Exploring fault diagnosis methods for mechanical equipment with weak dependency on annotated data is essential for industrial production. Contrastive learning (CL), capable of learning representations without labeling information, has achieved satisfactory performance in mechanical fault diagnosis. However, current CL-based approaches mainly encounter two limitations. First, the pre-training stage uses either unannotated or annotated samples exclusively while the fine-tuning stage solely relies on annotated ones, leading to inefficient sample utilization. Second, the representation learned by contrastive loss alone in the pretext task is sub-optimal for downstream diagnostic tasks. To address these issues, this paper proposed a novel diagnostic framework based on label-guided contrastive learning (LgCL) and weighted pseudo-labeling (WPL) strategy to improve fault diagnosis accuracy. In the pre-training stage, the proposed LgCL integrates two types of contrastive loss together with classification loss, enabling the encoder to learn discriminative representations that directly benefit the downstream diagnostic task. The devised hybrid fine-tuning strategy allows both labeled and unlabeled data to participate in fine-tuning via pseudo-labeling, enhancing model generalization. The pertinently designed WPL strategy mitigates the defect of noisy pseudo labels. Comparison and ablation experiments on two public datasets and one self-designed dataset validate the superiority of the proposed method for fault diagnosis with limited annotated data, with diagnostic accuracies improved by 25.30%, 5.47% and 10.02% over supervised, semi-supervised and contrastive learning methods, respectively.

Improving thermal shock and oxidation resistance of Cr3C2/WC-NiCr cermet coating by embedding large NiCrAlY superalloy particles

Cr3C2/WC-based cermet coating is widely used on the surface of mechanical equipment to strengthen its wear and corrosion resistance. Improving the high-temperature performance of cermet coating is the key to its industrial application. In this study, a novel type of bimodal microstructure cermet coating with the design of functional sites was fabricated via high-velocity air fuel (HVAF) spray technology. The toughness NiCrAlY superalloy particles are introduced into the traditional Cr3C2/WC-NiCr cermet structure to construct the thermal stress release site, which significantly improved the thermal shock resistance and thermal oxidation properties of the coating. The addition of NiCrAlY superalloy particle reduces the porosity of Cr3C2/WC-NiCr coating to 0.29 % and creates high-quality interface bonding and surface passivation layer. The newly structured cermet coating exhibits outstanding high-temperature performance. In a 1000 °C oxidation environment, the new coating remained intact after 120 h of exposure, while the traditional Cr3C2/WC-NiCr cermet coating exfoliated completely after 48 h of exposure. Further, in the thermal shock condition at 1000 °C, the lifetime of the new coating is 50 times greater than that of the traditional cermet coating, displaying excellent ability to relieve thermal stress. Additionally, the new coating shows good wear resistance and stability during high-temperature thermal exposure. After thermal exposure for 24 h and 72 h, the wear rates of the coating are 5.28 and 5.83 × 10−5 mm3 N−1 m−1, respectively, which is slightly lower than that of the as-sprayed coating. This study provides guidance for the improvement of high temperature resistance of thermally sprayed Cr3C2/WC-based cermet coatings.

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

Analysis of Cracking Causes of A Vibrating Screen Excited Beam

Abstract It was found that cracking occurred at the exciting beam of the vibrating screen produced by a mechanical equipment manufacturing Co., Ltd. In order to determine the reason for the cracking of the vibrating screen, the cracked vibrating screen samples were tested by appearance, physical and chemical properties, metallographic analysis, scanning electron microscopy(SEM) and energy spectrum analysis. The test analysis results show that the chemical composition analysis, tensile and flattening test results of the vibrating screen meet the requirements of GB/T 8163-2008 standard, the fillet weld at the vibrating screen exciting beam is not bevelled according to the requirements of the production design drawings. The main reason for the cracking of the vibrating screen is that the fillet weld cracking at the vibrating screen exciting beam originates from the welding defects in the weld, which accelerates the further expansion of the crack under the action of external vibration load, and finally leads to the complete cracking of the exciting beam weld.

A fractional-derivative kernel learning strategy for predicting residual life of rolling bearings

In the field of mechanical equipment maintenance, accurately estimating the remaining useful life (RUL) of rolling bearings is crucial for ensuring reliable equipment operation. However, prevalent deep learning methods face challenges such as limited sample sizes, and “black-box” mechanisms. To enhance the accuracy and interpretability of rolling bearing RUL prediction, a novel fractional-derivative kernel mean p-power error filtering algorithm (FrKMPE) is introduced. A comprehensive analysis of convergence for this method in terms of both mean error and mean square error criteria is provided. By combining the memory properties of fractional-derivative with the adaptability of kernel method, it can effectively capture features of non-stationary signals and sensitively monitor changes of rolling bearing health states (HSs). The effectiveness of the FrKMPE is validated through its application to the prediction of RUL using the IEEE PHM 2012 challenge dataset and the XJTU-SY dataset. Experimental results demonstrate that the proposed FrKMPE outperforms existing kernel adaptive filtering and deep learning methods in rolling bearing RUL prediction. The proposed method has advantages in dealing with complex nonlinear data and improving prediction accuracy, and provides a new perspective and solution for rolling bearing RUL prediction.

Envelope spectrum neural network with adaptive domain weight harmonization for intelligent bearing fault diagnosis under cross-machine scenarios

Accurate bearing fault diagnosis technology is highly important for ensuring the safe operation of mechanical equipment. Fault diagnosis methods can be roughly divided into signal processing-based methods (SPM) and data-driven methods (DDMs), which rely on physical knowledge and data knowledge, respectively. However, SPM cannot adapt to large data samples and dynamic parameter variations. DDMs did not consider physical representation consistency. To address the above issues, an envelope spectrum neural network (ESNN) is proposed for cross-machine bearing fault diagnosis. First, a deep transfer convolution network (DTCN) is constructed as the basic diagnostic framework. Second, a knowledge-to-model conversion strategy (KMC) is built to create ESNN, which utilizes equivalent convolution functions to construct the first two layers of DTCN, guiding the model to learn physical knowledge. Subsequently, an adaptive domain weight harmonization (ADWH) mechanism is proposed that can dynamically combine marginal distributions and joint distributions and automatically learn hidden features contained in physical and data knowledge, thus alleviating domain shift issues. Experimental evaluations are conducted using fault datasets from three different machines. The results show that, compared to models without knowledge guidance, ESNN achieves a 6.4% improvement in diagnostic accuracy. Compared to many advanced models, ESNN can achieve a maximum diagnostic accuracy improvement of 35.94%. The code of the ESNN can be found at https://github.com/John-520/ESNN.

The compression deformation and particles removal of PVA brushes during the post-CMP cleaning process

Abstract In advanced wafer manufacturing processes, a wet cleaning system (wet in dry out) integrated with chemical mechanical polishing equipment is the primary form of post-chemical-mechanical polishing (CMP) cleaning. This pioneering study addresses the quantitative description of the contact between a polyvinyl alcohol (PVA) sponge brush and the wafer surface and establishes a relationship between the compression deformation as a function of the pressure on the wafer surface to achieve precise control of the contact between the brush and the wafer. The physical properties of the three types of PVA sponge brushes, namely, porosity, saturated water absorption, and pore change rate, were analyzed, and the effect of particle removal was explored. Finally, the effect of the brush on removing the residual polishing liquid from the wafer surface under different compression deformation conditions was studied, and its internal mechanism was explained. A ‘√’ curve was observed between the compression deformation (stress) and particle removal effect. When the compression deformation (stress) is 0.5 mm (35.11 g), the 26 nm particles on the wafer surface after post-CMP cleaning can reach less than 10ea.

Fault identification of rolling bearing based on improved salp swarm algorithm

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

A novel remaining useful life prediction based on transfer hybrid deep neural network under variable working conditions

Deep learning-based methods for remaining useful life prediction (RUL) usually require the precondition that the training and test data obey the same distribution. In engineering applications, mechanical equipment is frequently under different working conditions, which can lead to significant differences in the distribution of collected data and difficulties in obtaining labels. This paper proposed a novel RUL prediction method based on transfer hybrid deep neural network to solve the above problems. Firstly, a degradation feature extraction strategy and a clustering hybrid feature screening strategy are proposed to enrich the information content of degradation features and obtain manual features with significant degradation trends. Then, a multi-stage shrinkage attention temporal convolution network is used to adaptively extract strongly expressive and information-rich deep features from the raw data. Next, a bidirectional convolutional gated recurrent unit based on bidirectional learning and convolutional operations is designed to achieve the fusion of manual and deep features and improve the quality of degradation features. Finally, the unsupervised domain adaptation strategy is used to reduce the differences in the distribution of degradation features between training and test data and to achieve feature alignment. This paper validates the effectiveness of the proposed method on six transfer tasks. The experimental results show that the RUL prediction effectiveness of the proposed method is better than other methods.

Multi-view graph convolutional networks based on multi-source information fusion for mechanical fault diagnosis

Data-driven intelligent diagnosis methods have demonstrated significant success within prognostics and health management systems due to their powerful feature representation and non-linear fitting capabilities. However, most existing methods employ simple fusion strategies and neglecting data structure considerations, which lead to incomplete feature representations for final diagnostic decisions. In this article, we address these limitations by proposing a multiview graph convolutional network [Formula: see text] framework based on multi-source information fusion for fault diagnosis of key components in mechanical equipment. This is achieved through joint research on sample graph and feature graph learning. First, convolution neural networks named the SharedNet module are leveraged to extract multi-source features, and then the deep features are concatenated in a fully connected layer. After that, we construct multi-view initial graphs to combine the correlations between samples and features to mine rich graph topology structure. Then, we redesign the graph learning strategy to further increase the robustness and generalizability of the proposed method. Finally, extensive experimental results on the multi-information datasets show that our proposed [Formula: see text] can not only outperform the state-of-the-art methods of comparison but also extract multi-view graph topology and feature representations for fault diagnosis.