Reliability Of Mechanical Equipment Research Articles

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.

Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels

Intelligent fault diagnosis can enhance the reliability of mechanical equipment. However, only a few labels are available in a large amount of fault data due to high labeling costs in practical engineering. The fault recognition capability of existing semi-supervised diagnosis methods is severely insufficient with limited labels, especially with extremely limited labels that only a single labeled sample available per fault type. To address this issue, a novel semi-supervised ensemble fault diagnosis framework termed ADAE-LFDM is proposed based on adversarial decoupled auto-encoder (ADAE) and low-dimensional feature distance metric (LFDM). Firstly, the locally selective combination in parallel outlier ensembles (LSCP) method is introduced to efficiently separate normal and fault samples. Subsequently, an ADAE with branching structure and latent space feature regularization strategy is proposed to decouple and capture the fault feature. Finally, a LFDM strategy that contains feature dimensionality reduction, and centroid-based metric is performed to achieve high-accuracy fault diagnosis. Experimental results based on two rotating machinery datasets have demonstrated that the proposed method achieves a diagnostic accuracy of over 97 % when there is only a single labeled sample available per fault type, and an average diagnostic accuracy of 85 % under cross-operating condition, showing the superiority compared to other methods.

Application of piezoelectric self powered wireless sensor in CCAL vibration environment monitoring

With the growth of the economy and social progress, people's requirements for mechanical equipment are becoming higher and higher, and the complexity of mechanical equipment is also increasing. To improve the safety and reliability of mechanical equipment, the establishment of Wireless Sensor Network (WSN) with the function of monitoring and self energy supply in vibration environment is an effective way to solve this problem. This study presents a piezoelectric self-powered wireless sensor monitoring system with a piezoelectric self-powered circuit model, and utilizes the FFT algorithm to reduce power consumption and achieve a sleep-wake-sleep cycle intermittent periodic operation mechanism for low-power devices. The results show that the charging speed of the PISS structure is 2.4 %, 65.8 %, and 80 % faster than the standard, SISS, and SCE structures, respectively. Compared with the triangular cantilever, the output voltage and output power of the rectangular cantilever have increased by 59.5 % and 80.9 %, respectively. When the acceleration is 1 g, the output power and output voltage are 0.5 mW and 6 V, respectively. When the acceleration is 2 g, the output power and output voltage are 2.2 mW and 12 V, respectively. The above results indicate that the piezoelectric self-powered wireless sensor monitoring system in this study has a certain reference value in the field of self-powered wireless sensors.

A novel fault early warning method for mechanical equipment based on improved MSET and CCPR

Prompt fault early warning is a significant approach to improve the reliability of mechanical equipment. An abnormal state recognition method based on improved multivariate state estimation technology (MSET) and control chart pattern recognition (CCPR) is proposed. First, the health data band (HDB) of equipment is established through a k-nearest neighbor algorithm. Meanwhile, a dynamic memory matrix construction approach based on a clustering algorithm is presented to improve the calculation efficiency of MSET. The CCPR is integrated to reduce limitations such as artificial interference in the traditional threshold method of fault early warning. The proposed method is applied into two cases of rolling bearing and control moment gyro so as to compare its performance with other MSET modeling methods. According to the results, the proposed method can detect equipment abnormalities hours to weeks in advance, and significantly reduce the false alarm rate and calculation time.

Research on the characteristics of micro planar capacitance sensor for multi wear particle detection

Wear particles are rich in information about the running state of mechanical equipment. Identifying wear particles is of great importance for monitoring and maintaining the safety and reliability of mechanical equipment. This paper investigated the characteristics of the micro planar capacitance sensor used for wear particle detection. The structural parameters of the micro planar capacitance sensor were optimized through finite element numerical simulation. The influences of excitation frequency and particle velocity on the sensor characteristics were studied through different experiments. The results show that when the excitation frequency is 1.8 MHz, and the particle velocity is 4 mm/s, the sensor has higher sensitivity for particle recognition. The relationship between the sensor signal pulse width and particle velocity is a power function. The sensor can distinguish and detect various metal and non-metallic particulate pollutants and recognize 78 μm iron particles and 165 μm copper particles. This study provides a new method for wear condition monitoring.

New RUL Prediction Method for Rotating Machinery via Data Feature Distribution and Spatial Attention Residual Network

The prediction of remaining useful life (RUL) is one of the important measures to ensure the safety and reliability of mechanical equipment. Aiming at the low accuracy of residual life prediction caused by difficulty in feature extraction from high data redundancy in the monitoring process of rotating machinery, an RUL prediction method based on data feature distribution and spatial attention residual network (SARN) is proposed. In the proposed method, multi-sensor data is segmented utilizing distribution feature difference analysis, so as to highlight the degradation characteristics in the sample sequence. Combined with the proposed SARN based on a spatial attention mechanism and residual network, more extensive and complementary features can be extracted. It not only focuses on the importance screening of feature information from all input data, but also realizes the mapping between global sample features and labels, to improve the accuracy of RUL prediction. Finally, the method is verified by the experiments on PHM2012 and C-MAPSS datasets. The results indicate that, compared with the CNNLSTM method on C-MAPSS dataset, the proposed method has an average reduction of 14.37% in MAE index, and an average increase of 22.59% in R2 index. This shows that the proposed method has satisfied RUL prediction performance for rotating machinery.

A Calibration-Based Hybrid Transfer Learning Framework for RUL Prediction of Rolling Bearing Across Different Machines

The effective remaining useful life (RUL) prediction of rolling bearings could guarantee mechanical equipment reliability and stability. The hybrid physical and data-driven prognosis model (HPDM) is recently receiving increasing attention. However, HPDM approaches suffer from two significant challenges that limit their applicability to complex prognosis scenarios: (1) the reality gap between the simulation and measurement data and (2) the limited model generality to accommodate different working conditions and machines. From the perspective of leveraging physical model inference as ’teachers’ for the data-driven model, this article proposes a calibrated-based hybrid transfer learning framework to improve the data fidelity and model generality. Firstly, a 5-DOF dynamic model of rolling bearing is constructed. Comprehensively considering the crack and spall behaviors of degradation evolution, the physical model could provide various failure trajectories. Secondly, the particle filter-based calibration is proposed to retain the high fidelity of the physical simulation. Finally, a Physics-informed Bayes Deep Dual Network (PI-BDDN) is designed. The designed network fuses the physical calibrated simulation as augmented input space to learn representative prognosis features and makes the transfer learning process interpretable by combining the physical model parameters into adversarial learning to selectively identify the most informative knowledge for RUL prediction. The effectiveness of the proposed method is verified on two representative bearing datasets, and comparative results show the superiority of the proposed method on prediction accuracy and uncertainty quantification.

A Novel Semi-Supervised Graph-Guided Approach for Intelligent Health State Diagnosis of a 3-PRR Planar Parallel Manipulator

As one of the most important operation and maintenance approaches, health state diagnosis technology plays a crucial role in ensuring the safety and reliability of mechanical equipment. The planar parallel manipulator, as a typical actuator, is widely employed in the field of precision manufacturing due to its advantages of high stiffness, large load support capability, and high precision. However, compared with common key functional components (such as bearings and gearboxes), planar parallel manipulators have more complicated operating mechanisms and failure behaviors. To satisfy the health state diagnosis demands of planar parallel manipulators in the scenario of insufficient label information, a novel intelligent health state diagnosis approach, termed semi-supervised graph-guided network with perception attention (SGN-PA), is developed for a 3-PRR (P and R represent prismatic and revolute pairs, respectively) planar parallel manipulator. Specifically, an improved multiorder graph perception module is constructed to extract multiscale feature information, and achieve feature fusion by combining perceptual attention mechanism, which enables the proposed SGN-PA model to have adaptation adjustment capabilities. Following that, local and nonlocal feature constraint strategies are employed with pseudo-label technology to reduce intraclass differences and maximize interclass differences, and then to fit the demands of health state diagnosis tasks. Eventually, based on the simulation and experimental scenarios of a 3-PRR planar parallel manipulator, the effectiveness and feasibility of the proposed SGN-PA model is extensively confirmed, and the diagnosis results show that it can significantly relax the constraints of label information while maintaining superior performances.

A novel distinction method of metal debris material based on inductive sensor with multi-sensing units

Condition monitoring is an important guarantee to improve the reliability of mechanical equipment. Material information of debris can be used for fault location, but the distinction technology of debris material is a weakness in oil monitoring. A novel distinction method of metal debris material based on inductive sensor with multi-sensing units is proposed in this article. There are at least two sensing units with various amplitude-size curves, and the judgment result will be more accurate with the number of sensing units. Soft magnetic materials can change the trend of amplitude-size curves, which is beneficial to the construction of the required debris sensor. The experimental results show that this method can identify the debris information about the material and size based on data comparison. This method is of great significance for fault diagnosis of rotating and reciprocating machinery.

The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data

The remaining useful life (RUL) prediction provides an essential basis for improving mechanical equipment reliability. In practical application, the variant of working conditions and incomplete degradation data seriously deteriorate the performance of the prognostic models. In order to conquer this problem, a two-stage RUL prediction method is proposed for the cross-domain prognostic task with insufficient degradation data. At first, the two-level alarm mechanism is employed to detect the first predicting time (FPT) of each mechanical entity adaptively. Then, the deep separable convolutional network with the double transferable attention mechanism (DSCN-DTAM) is proposed to construct the cross-domain prognostic model. In DSCN-DTAM, multiple regularization strategies can guide the model to extract domain-invariant features, and the double transferable attention mechanism is designed to select the degradation information with high transferability. Finally, the proposed method is verified by multiple transfer prognostic tasks designed by two bearing datasets. Compared with other methods, the proposed method shows superior performance.

Fault Diagnosis Method of Rotating Machinery Based on Collaborative Hybrid Metaheuristic Algorithm to Optimize VMD

With the improvement of the complexity and reliability of mechanical equipment, it has been difficult for the commonly used variational modal decomposition method of vibration signal of rotating machinery to meet the current practical engineering requirements. In order to further improve the adaptability, processing efficiency, and robustness of rotating machinery fault diagnosis methods, a collaborative hybrid element heuristic to multiobjective optimization algorithm is introduced in this paper. Combined with variational modal decomposition (VMD) method, the fault diagnosis method of rolling bearing under complex working conditions is studied. This paper mainly uses a collaborative hybrid metaheuristic algorithm to improve the nondominated sorting genetic algorithm II (NSGA II) and multiobjective particle swarm optimization (MOPSO), which improves the convergence efficiency of multiobjective optimization method and solves the problem of uneven distribution of optimal solutions. Then, the improved multiobjective optimization algorithm is combined with VMD to solve the problem of parameter selection of the VMD method under complex working conditions of rotating machinery. At the same time, the variation relationship between various signal features and VMD decomposition results is compared and studied, and the features with good effect are taken as the objective function of the optimization algorithm; the ability of denoising and feature extraction of VMD in rotating machinery fault diagnosis is improved. In this paper, the proposed method is explored by using analog signals and experimental data of rolling bearings. Through comparison, the improvement of adaptive ability, operation speed, and robustness of the proposed method in rotating machinery fault diagnosis is verified.

Mechanical Equipment Reliability Analysis: Case Study

In civil and mining industries, Wheel loaders are an important component and their cost capability at effective operation. The environmental and operational factors dramatically affect the performance of loaders. In many cases, failure data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way they can affect the item's reliability are known. However, some factors that may affect the item's reliability are typically unknown and lead to unobserved heterogeneity. These factors are categorized as unobserved covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to erroneous model selection for the time to failure of the item, as well as wrong conclusions and decisions. There is a lack of sufficient knowledge, theoretical background, and a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. The unobserved covariates will be analyzed using frailty models (Such as Mixed Proportional Hazard).A case will illustrate the application of the framework.

A novel feature adaptive extraction method based on deep learning for bearing fault diagnosis

Bearing health condition directly affects the reliability of mechanical equipment. Although deep learning (DL) algorithms have achieved great results in the field of bearing fault diagnosis, traditional activation function uses a fixed mathematical formula to achieve non-linear feature transformation, which tend to compress part of the effective fault information and reduce the performance of fault diagnosis. To address this problem, this paper proposes a slope and threshold adaptive activation function with tanh function (STAC-tanh). Establish the relationship between non-linear feature transformation and input signal by automatically adjusting the shape of activation function. Finally, the model can retain valid fault information to improve fault diagnosis performance. Then, combining STAC-tanh and Residual Networks, this paper proposes ResNet-STAC-tanh for bearing fault diagnosis. Experimenting on the two bearing datasets with added noise, the average accuracy of the network reached 90.00% and 90.77%, respectively. The effectiveness of the new method was verified through comparative experiments.

Kernel extreme learning machine based hierarchical machine learning for multi-type and concurrent fault diagnosis

The detection and identification of faults in rotary machines are of great significance to the mechanical equipment reliability especially the gearbox. Traditional machine learning algorithms suffer from low diagnosis accuracy of faults that have multiple types and exist concurrently. A novel machine learning method called hierarchical machine learning (HML) was proposed in this study to improve the faults diagnosis accuracy. The proposed algorithm consists of two layers. The first layer comprises a traditional machine learning model to identify the faults with distinguishable features and filter out these faults with indistinguishable features. The second layer model recognizes the faults filtered out by the first layer. In order to verify the effectiveness of the proposed method, the gearbox simulation experiment is carried out in the study. The simulation results validate that the proposed method outperforms other algorithms under an identical measure.

The necessity of conducting repairs, or the Economic paradox

The increase in the total number of machines in the middle of the 20th century led to a rise in the number of repair services and served as an impetus for the start of work to improve the reliability of mechanical equipment based on the statistical probabilistic approach, the provisions of terotechnology and methods of technical diagnostics. Maintaining machines and mechanisms in working condition has become quite expensive. On the other hand, reducing the cost of maintenance and repairs leads to a violation of the continuity of the technological cycle. Existing trends in mechanical engineering and in the general paradigm of the development of civilization indicate a tendency to reduce the durability of parts and units. The manufacture of products with a long service life becomes economically unviable, which is the essence of the paradox. The article considers the examples of implementation of various approaches to the operation of equipment during maintenance and repair. The prerequisites for the necessity of conducting repairs are analyzed. It is recommended to try to reach a compromise between the manufacturer and the consumer on the basis of long-term contracts for the supply of spare parts or the provision of equipment maintenance services. The urgency of the problem is further justified by the high rate of robotization, since robots also need to be repaired. In this regard, the authors express the hope that this article will be useful in solving the problem of the repair.

Research on a Noise Reduction Method Based on DTCWT and the Cyclic Singular Energy Difference Spectrum

The gear is the most important part of the transmission system of mechanical equipment, and the monitoring and diagnosis of it can improve the reliability of mechanical equipment. However, mechanical equipment generally works in harsh working conditions. The gear vibration signal is subjected to strong noise interference in working conditions, which brings great challenges for the effective diagnosis of gear faults. This paper proposed a noise reduction method based on the dual-tree complex wavelet transform (DTCWT) and cyclic singular energy difference spectrum. First, the gear vibration signal containing strong noise interference is decomposed into a series of signal components with different frequency characteristics by using the time-frequency analysis ability of DTCWT. Then, cyclic singular energy difference spectrum is proposed based on the idea of a cascaded cycle and the successive elimination of noise interference to process each signal component with different frequency characteristics, and the termination conditions of cyclic singular energy difference spectrum can be set according to the noise interference distribution characteristics in different frequency bands. The final noise reduction of the original gear vibration signal can be realized based on signal reconstruction after the noise reduction processing of each signal components with different frequency bands. Finally, experiments are carried out to verify the effectiveness of the proposed method, which is effective and suitable for the noise reduction of the vibration signal.

Fault diagnosis method of rolling bearing based on the multiple features of LMD and random forest

The occurrence of rolling bearing fault not only affects the operation efficiency of mechanical equipment, but also leads to the safety accidents. The research on fault diagnosis of rolling bearing is of great practical significance to improve the reliability of mechanical equipment, avoid unexpected faults and ensure safety in production. In this paper, a fault diagnosis method of rolling bearing based on the multiple features of local mean decomposition (LMD) and random forest is proposed. It is suitable for processing non-stationary vibration signals generated by rolling bearings and adaptively decomposing the characteristic information excited by faults into a series of product function (PF). Then, the mixed feature vectors of each PF are constructed from different angles to realize the extraction and quantification of the rolling bearing fault feature information. Finally, combined with the random forest algorithm, accurate identification of the type of rolling bearing failure is achieved. The overall recognition rate reached 94.4%. It proves that the method proposed in this paper is effective.

Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model

There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.

Fault identification method for planetary gear based on DT-CWT threshold denoising and LE

Planetary gear is widely used in large-scale complex mechanical systems. However, because of the particularity of planetary gear transmission, serious wear and fatigue crack failures often occur in the sun gear, planet gears, and inner gear ring. In addition, every type of fault will experience different degradation processes. Improving the operation reliability of mechanical equipment through fault diagnosis of planetary gears and monitoring their degradation process is beneficial. This paper proposes a planetary gear fault identification method based on Dual-tree Complex wavelet transform (DT-CWT) threshold denoising and Laplacian eigenmaps (LE). The noise reduction processing of the original signal is achieved by the DT-CWT threshold denoising method, which takes full advantage of DT-CWT and is combined with the wavelet threshold of rigrsure principle. The original high-dimensional feature set, including the time domain features, frequency domain features, permutation entropy, and fractal box dimension of the denoised signal, is constructed from multi-angles. To solve the problems of excessively large feature dimension and the existence of redundant information, the LE algorithm is used to reduce the dimension of the original high-dimensional feature set, and the low-dimensional sensitive features are obtained. Through the above method, the effective identification of different fault states and different degradation states of the planetary gear are achieved.

Remaining life prediction of rolling bearing based on PCA and improved logistic regression model

Rolling bearing reliability assessment and remaining useful life (RUL) prediction are crucially important for improving the reliability of mechanical equipment, reducing the probability of sudden failure, and saving on maintenance costs. Novel prediction method is proposed based on PCA and Improved Logistic Regression Model (ILRM) to solve the problem that the model is difficult to establish and the remaining life of rolling bearing is difficult to estimate. Time domain, frequency domain, and time-frequency domain feature extraction methods are employed in this study to extract the original features from the vibration signals. Next, the relative feature value is used to reduce the influence of random vibration and individual differences between bearings. PCA is run based on the original extracted features and high dimensional and superfluous information to merge the original features and reduce the dimension, where typically sensitive features are extracted. The ILRM is then used to build a model that reflects the deterioration trend and eliminates the impact of fluctuations, ultimately yielding information regarding the rolling bearing’s reliability and the remaining life. The proposed method is shown to accurately predict the lifespan of rolling bearings, thus exhibiting practical value in the engineering field.