Wear Fault Research Articles

Analysis of Wear Mechanism and Fault Characteristics of Planet Gears with Multiple Wear Types in Planetary Gearbox

Analysis of Wear Mechanism and Fault Characteristics of Planet Gears with Multiple Wear Types in Planetary Gearbox

Study on the Sensitivity of Detachable Wear Particle Sensor Based on Iron-Based Amorphous Soft Magnetic Rings

A parallel three-coil wear particle sensor is innovatively proposed with the detachable substrate and magnetic rings. The magnetic rings are designed and fabricated using polydimethylsiloxane (PDMS) and iron-based amorphous soft magnetic material (IASM). The excitation coils surround the magnetic rings, leaving only a 1mm air gap on the inner side (near the flow channel). The magnetic rings are magnetized under the action of the high-frequency alternating magnetic field, which forms an enhanced magnetic field with a high gradient distribution in the detection area. Through numerical simulation and experimental verification, the influence of IASM concentration, radial position and size of wear particles on the sensor output signal are studied. With the increase of IASM concentration, the sensor detection sensitivity rises dramatically. The sensor with 75&#x0025; IASM magnetic rings can effectively detect 60 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> iron particles and 160 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> copper particles in a 10mm flow channel; the detection error rate is less than 15&#x0025; in the actual lubrication circuit, which fully meets the needs of early abnormal wear fault diagnosis of mechanical equipment. Therefore, this method is a promising candidate for developing the large-flow electromagnetic wear particle sensor sensitivity.

Research on Common Fault Diagnosis and Classification Method of Centrifugal Pump Based on ReliefF and SVM

The centrifugal pump is an important rotating machine and it is very critical to identify and differentiate among its common faults as quickly and accurately as possible. Based on the ReliefF algorithm and the sparrow search algorithm (SSA) in conjunction with support vector machine (SVM), an approach for faults classification and diagnosis of centrifugal pumps is proposed, its advantages over traditional fault diagnosis methods include a reduction in the number of characteristic parameters, shorter diagnosis times, as well as improved classification accuracy and robustness. We collected the fault data by designing a centrifugal pump fault test bench that recorded vibration signals for the rotor misalignment fault, the rotor unbalance fault, the seal ring wear fault, and normal operating conditions, and preprocessed the collected signals with Kalman filtering to remove noise interference, the time domain characteristic indexes and the frequency domain characteristic indexes of the filtered signal were extracted, each feature index is given a distinct weight using the ReliefF method, and the eigenvalues with weights less than the threshold are deleted, and the feature indexes that remain create a defect feature matrix. Particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing algorithm (SA) were used to optimize the SVM for comparison in order to verify the SSA-SVM model’s performance for fault diagnosis. The comparison results show that the model has high recognition accuracy, short Classification time, and strong robustness.

An intelligent belt wear fault diagnosis method based on deep learning

ABSTRACT Belt conveyors are important transportation equipment in coal mining enterprises. At present, most research on this topic focuses on areas such as tear resistance and foreign body identification. Few studies have focused on belt wear, but belt wear is the subject of daily inspections on site. The artificial grayscale analysis method, support vector machine (SVM) method, and deep learning network are proposed herein to identify the degree of belt wear by using image acquisition devices installed on belt conveyors to collect images of no-load belts, instead of manual inspection. The experimental results indicate that the grayscale analysis method has limitations in identifying belt wear. For complex types of wear, such as annular wear, its recognition capability is poor, and the grayscale analysis method is heavily dependent on the results of human analysis. The highest accuracy of the SVM method is 84.5%, and it effectively identifies complex wear states. After training, worn belts can be detected automatically. However, the selection of features during training completely depends on human decisions, and the accuracy is affected by such factors that have a great influence. The deep learning network attained a 91.5% average recognition accuracy rate with the highest accuracy being 95%. It can fully automate intelligent feature selection, training and detection.

Energy features fusion based hydraulic cylinder seal wear and internal leakage fault diagnosis method

Internal leakage is one of themost common faultsinhydraulic cylinders, and seal wear is the main factor in internal leakage. However, it is difficult to detect seal wear and internal leakage in hydraulic cylinder using present approaches due to the complex hydraulic system. Therefore, an intelligent fault diagnosis method based energy features fusion is proposed to detect seal wear and internal leakage. First, computational fluid dynamics(CFD) technology was adopted to analyze the flow field in the internal leakage area of hydraulic cylinder, and it was found that energy features of pressure signal are related to internal leakage. Then, wavelet packet transform is appliedto extract energy features of pressure signal. Finally,energy featuresis decomposed into statistics by multivariate statisticstheory. Statistics are used to detect piston seal wear and internal leakage.The proposed method creativelystudiesseal wear and internal leakage from the perspective of flow field analysis, which doesnot requirealarge number of faultsamplesand complicated parametersoptimization. Experimental investigations are performed to validate the performance of the proposed approach. It is shown that the proposed method has much more robustness and accuracy than several classical fault diagnosis methods. The study does provide an effective way to detect seal wear and internal leakage in hydraulic cylinder.

Nonlinear dynamic analysis of high-speed gear pair with wear fault and tooth contact temperature for a wind turbine gearbox

This paper aims to reveal the relationship between the high-speed gear wear and nonlinear dynamics for a wind turbine gearbox considering the tooth contact temperature. The temperature and deformation of the tooth surface are calculated respectively using the Blok's flash temperature theory and thermal deformation formula, and the meshing stiffness caused by the tooth contact temperature is obtained using the Hertz theory. The wear depth and time-varying meshing stiffness of high-speed gears are calculated using the Archard's equation and potential energy method, respectively. The nonlinear dynamic model of the gearbox is established to study the characteristics, and the frequency domain values of gear wear are compared with experimental data. The results show that the tooth contact temperature makes the time-varying meshing stiffness decrease, then affecting the dynamics of the system, the stability decreases with the increase of comprehensive transmission error, and for the gearbox considering the contact temperature, the gear wear makes the chaotic motion occur in advance and its region increase obviously. The research provides a basis for the wear fault diagnosis and detection of gear transmission systems.

Research on Electrostatic Monitoring of Tribo-Contacts with Dynamic Adaptive Fusion Method

Electrostatic monitoring is a unique and rapid developing technique applied in the prognostics and health management of the tribological system based on electrostatic charging and sensing phenomenon. It has considerable advantages in condition monitoring of tribo-contacts with high sensitivity and resolution. Unfortunately, the monitoring result can be affected due to the switch of operating conditions that reduces its accuracy. This paper presents a dynamic adaptive fusion approach, moving window local outlier factor based on electrostatic features to overcome the influence. Life cycle experiments of rolling bearings and railcar gearbox were carried out on an electrostatic monitoring platform. The MWLOF method was used to extract and analyze the experimental data, combined with the Pauta criterion to judge wear faults quantitatively, and compare with other feature extraction results. It is verified that the proposed method can overcome the influence of changes in working conditions on the monitoring results, improve the monitoring sensitivity, and provide an accurate reference for friction and wear faults.

Evolving Connectionist System and Hidden Semi-Markov Model for Learning-Based Tool Wear Monitoring and Remaining Useful Life Prediction

Tool wear can cause dimensional accuracy and poor surface quality in milling process. During the operation of tool wear, it can also cause breakage and damage of the workpieces. To prevent these conditions, it&#x2019;s important that the tool wear is monitored and the remaining useful life (RUL) is predicted in real time. In this paper, time domain and frequency domain statistical features are firstly extracted using multi-sensory fusion method, including the cutting force, vibration and acoustic emission sensor. Seven eigenvectors are selected as the input of the prediction model based on the distance correlation coefficient between 140 feature vectors and the wear value, which provide the most sensitive features to wear faults. The paper establishes a nonlinear relationship between high-dimensional feature vectors and tools wear based on the evolving connectionist system (ECoS), which uses the incremental learning algorithm to realize real-time prediction of the tools wear. Finally, using the wear value predicted by ECoS as hidden state sequence of Hidden Semi-Markov Model (HSMM), the RUL prediction of the tool based on HMM is established. The 2010 PHM challenge data were used to train the model. The experimental result shows that in comparison with artificial neural network, the ECoS model has higher prediction accuracy, and its mean RMSE error for three tools is 14.8. In comparison with the RUL prediction of HMM model, Probability-based RUL prediction of HSMM is more stable.

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

Early Wear Fault Dynamics Analysis Method of Gear Coupled Rotor System Based on Dynamic Fractal Backlash

Abstract The aim of this paper is to establish a typical gear coupled rotor system model and give an analysis of the early wear of the tooth surface. The tooth surface wear will cause the change of the backlash. Different from previous studies on the backlash, the backlash in this paper is a dynamic fractal backlash, which is obtained based on the fractal wear model of tooth surfaces. Firstly, based on the FA (Flodin and Andersson) model and our previous studies on the contact analysis of rough surfaces, a fractal wear model of tooth surfaces is established. Then, the system vibration response of embedded dynamic fractal backlash is compared with that of embedded fixed backlash. The results show that the traditional fixed backlash cannot reflect the small changes of the system response, while the dynamic fractal backlash can sensitively reflect changes of dynamic characteristics of the system, so this kind of mathematical model considering dynamic fractal wear will be more conducive to the analysis and prediction of gear wear faults in engineering.

Research on 3D reconstruction method of wear particle dynamic image based on multi contour space mapping

Aiming at the problems of current image monitoring methods of lubrication oil wear particles, this paper designs and builds a dynamic monitoring system for oil wear particles based on microfluidic microscopic images. A contour-based 3D reconstruction method of debris particle images is proposed. The image sequences of rotating wear particles tracked by a single target are used as data, and the contour of the wear particle is extracted and the data is stored. The minimum area external rectangle method is used to correct the rotation of the particle images for the problem of deflection. And an algorithm based on cylindrical coordinate space conversion is used to convert the discrete contour point data into three-dimensional space. Complete the 3D model reconstruction of microfluidic wear particles. The ability to analyse wear particles in oil online monitoring technology is improved, which also shows new ideas for wear status monitoring and fault diagnosis technology.

Experimental investigation on the effect of misalignment on the wear failure for spline couplings

The teeth of misaligned spline couplings used in power transmission parts of helicopter and aero-engine often suffer serious abnormal wear faults during service. Through the self-made spline coupling test rig, the mechanism of misalignment on the spline coupling wear was explored. The tooth contact domain of the spline coupling with shaft misalignment is close to the addendum, and the tooth contact domain of the spline coupling with angle misalignment is mainly concentrated on the side of the tooth end. The edge contact effect becomes more serious as the amount of misalignment increases, resulting in abnormal wear at specific positions on the tooth surface. The presence of misalignment increases the vibration intensity of the spline coupling based vibration signal monitoring, which will increase the wear of the teeth. In addition, the difference in system vibration response of different misalignment types can be used to identify the misalignment failure mode of the spline coupling. It can be inferred from the characterization of wear debris that fretting wear is the cause of the failure of the misaligned spline coupling. Tooth modification, surface strengthening modification and lubrication design are necessary as potential solutions to improve the wear resistance of misaligned spline couplings.

Dynamic modeling of fluid nonlinear compression loss and flow loss oriented to fault diagnosis of axial piston pump

Volumetric efficiency (VE) is an extremely important index to evaluate the performance of pump and motor. Efforts have been concentrated on the causes and character of leakage loss, but few quantitative analyses of pump kinematic clearances (KCs) identification and nonlinear compression loss caused by air content are discussed. This paper clarifies the nonlinear compression loss resulted from the air content of the oil in detail and proposes an improved flow loss model of the swash-plate axial piston pump (SAPP). Then, combining the improved model with experimental results, the KCs of SAPP are identified. It has been verified that a reasonable sampling path is useful for reducing sampling numbers and guaranteeing identification accuracy. Finally, the identification and quantization of the wear fault of SAPP are realized by analyzing the estimated value of KCs. The proposed method is simple and easy to implement and shows high predictive accuracy. It provides a possible and promoting technology support for the autonomous calibration of the system parameters and the active compensation of the system control.

Detection and analysis of metal scrap in lubricating oil based on wear fault of spindle bearing of turboshaft engine

There is a large amount of information about the wear failure status in the particles, and the current wear failure status can be judged by monitoring them. In this paper, the main shaft bearing wear failure causes the change of metal particles in the lubricating oil and further causes the deterioration of the bearing condition. Based on the lubricating oil metal chip particle test system on the bearing testing machine, the particle size distribution change data in the bearing oil is obtained and the metal oil The main bearing fault prediction model with particle number and particle size as characteristic parameters realizes the fault diagnosis and condition evaluation of main shaft bearing wear based on the lubricating oil metal chip particle test, thereby effectively providing technical support for turboshaft engine fault diagnosis.

Automatic representation and detection of fault bearings in in-wheel motors under variable load conditions

The wear fault of the inner and outer race of bearing in an in-wheel motor is vital as the performance of bearing effects the transmission efficiency. Unlike the commonly used vibration analysis-based monitoring methods, the accelerometer cannot be installed inside the in-wheel motor since it would damage the structural reliability. The electricity-related signals sourcing from the motor controller may be a feasible way. In this paper, a load demodulation and normalization method under the condition of no vibration sensor is proposed to solve the problem of fault detection and diagnosis of the in-wheel motor transmission component under the condition of variable load. First, the stator current is selected to reflect the load changes according to the mathematical model of in-wheel motor in the three-phase stationary coordinates. Second, the order tracking is applied to synchronize the sampled signals on time domain with synchronous resampling, which converts the equally spaced signals into the angularly spaced signals. The merits are improving the definition of time–frequency representations (TFRs) of wear fault and avoiding the difficulty to determine the resolution of TFRs caused by load fluctuations. The short-time Fourier transform (STFT) is introduced to convert the angularly spaced signals into the TFRs. Finally, an adapted convolutional neural network (CNN) with random weight initialization and dropout strategy is employed to classify the wear of inner race and outer race. The proposed framework is verified on the in-wheel motor simulation platform. The experimental results show that the proposed method has a higher fault detection precision than the other methods. The core contribution reveals that this paper does not use the vibration-free sensor. Instead, the electrical signals of the controller are used to realize the fault detection and diagnosis of the inner and outer wear of the in-wheel motor bearing under the variable load conditions. Compared with the commonly used fault diagnosis method based on vibration signal analysis, the proposed method is more suitable for the condition monitoring system of the in-wheel motor transmission mechanism.

Feature extraction based on PSO-FC optimizing KPCA and wear fault identification of planetary gear

Feature extraction based on PSO-FC optimizing KPCA and wear fault identification of planetary gear

Modeling and fault identification of the gear tooth surface wear failure system

Abstract In order to detect the gear tooth surface wear fault, this paper presents a new fault diagnosis method based on Symlets wavelet family multi-structure element difference morphological denoising and frequency slice wavelet transform (FSWT). Besides considering the gear backlash, time-varying mesh stiffness, gear error and bearing longitudinal response, and low frequency excitation caused by the torque fluctuation, random disturbance of damping gear ratio, gear backlash, excitation frequency, and meshing stiffness are also considered. Dynamics equations of a three degrees of freedom spur gear transmission system with tooth surface wear fault are established according to Newton’s laws. The 4–5 order variable step Runge–Kutta method has been used for solving the equations to get the vibration signal of the system. Then, the proposed method is applied to extract the wear fault signal, which verifies the feasibility and effectiveness of the proposed method.

Fault detection for sliding bearings using acoustic emission signals and machine learning methods

Driven by the potential application of sliding bearings under wear-and fatigue-critical operating conditions, i.e. in planetary gearboxes for wind turbines or automotive engines with start-stop systems, the reliability and lifetime prognosis of heavy loaded sliding bearings under low rotational speeds is an emerging field of research. The application of machine learning (ML) offers a great potential for all kinds of engineering applications when physical models are not feasible due to their complexity. This study showcases the application of ML to wear and fatigue fault detection and lifetime prognosis for sliding bearings using acoustic emission signals.

Fault parameter identification in rotating system: Comparison between deterministic and stochastic approaches

Fault identification is a recurrent topic in rotating machines field. The evaluation of fault parameters allows better maintenance of such expensive and, sometimes, large machines. Unbalance is one of the most common faults, and it is inherent to rotors functioning. Wear in journal bearings is another common fault, caused by several start/stop cycles – when at low rotating speed, there is still contact between shaft and bearing wall. Fault parameter identification generally uses deterministic model–based methods. However, these methods do not take into account the uncertainties inherently involved in the identification process. The stochastic approach by the Bayesian inference is, then, used to account the uncertainties of the fault parameters. The generalized polynomial chaos expansion is proposed to evaluate the inference, due to its faster performance regarding the Markov chain Monte Carlo methods. Deterministic and stochastic approaches were compared; all were based on experimental vibration measurements of the shaft inside the journal bearings. The Bayesian inference with the polynomial chaos showed reliable and promising results for identification of unbalance and bearing wear fault parameters.

A Segmented Preprocessing Method for the Vibration Signal of an On-Load Tap Changer

The vibration signal of an on-load tap changer (OLTC) consists of a series of sharp vibration bursts, and its fault feature in certain periods is easily missed. This study considered that preprocessing the vibration signal of the OLTC in segments could effectively solve the aforementioned problem. First, the collection of the signal is discussed, the waveform characteristics of the vibration signal when the OLTC was in normal action was described, and the selection of the signal was analyzed. Second, the time domain characteristics and frequency spectrum analyses were carried out to demonstrate the necessity of segmented preprocessing. Further, the segmented preprocessing method for the vibration signal of the OLTC was presented. Finally, the main mechanical faults of the OLTC were simulated, and the vibration signals were collected to carry out the fault diagnosis experiment on the OLTC. The experimental results showed that the accuracy of the fault diagnosis increased from 94.30% of the nonsegmented preprocessing to 98.46% of the segmented preprocessing. The increase was greater, especially for contact wear faults. The method was successfully applied to the actual project.