Gear Fault Research Articles

Fault Detection and Diagnosis of Gear Train Based on Motor Current Signature Analysis

Gear fault diagnosis generally uses oscillation signals to extract fault features, but the oscillation identify technique is inconvenient to set up sensors and is easily affected by the surrounding and noise. The motor itself has the property of a sensor, and signals such as motor current is able to reflect the change of the load torque. This paper proposes a gear fault detection approach, which applies the Motor Current Signature Analysis (MCSA) to exploit spectral characteristics to recognize malfunction in servo motor drive gears. First, we calculate the nominal revolutions per minute (rpm) to explore frequencies of interest. Second, frequency bands where faulty signals would be appear are constructed. Next, we extract power spectral density (PSD) feature data. Finally, the paper computes spectral metrics for the frequency band of interest. By using two spectral metrics, gear health and failure data are clearly grouped in different areas of the scatter chart. The experimental results give evidence of the effectiveness of analyzing current characteristics of servo motors to classify fault and health data.

Mesh characteristic analysis and dynamic simulation of spur gear pair considering corner contact and tooth broken fault

Theoretically, the contact of a spur gear pair occurs on the line of action (LOA), but the corner contact may happen due to the tooth deformation, especially when there is a tooth broken fault. In this paper, based on the tooth contact analysis, the initial gaps considering tooth broken fault are accurately calculated. Meanwhile, the initial gaps are brought into the loaded tooth contact analysis program and yield the time-varying mesh stiffness (TVMS) and non-load transmission error (NLTE). By comparing with the finite element (FE) method, the meshing state of the gear pair considering various tooth broken heights is presented. The result shows that it is inaccurate to regard the mesh stiffness value as zero when the missing tooth profile should have contact since serious corner contact phenomenon will occur in this state. Moreover, by comparing the simulated dynamic response with the experimental results, this paper finds that the simulation result considering the corner contact effect is closer to the experimental result. This study can provide a more accurate model for the diagnosis of gear tooth broken faults.

Research on gear crack fault diagnosis model based on permanent magnet motor current signal

To solve the problem that it is inconvenient to install additional sensors to detect fault signals on the gear of the shearer, the permanent magnet motor control model was first established, and the motor control parameters were determined. Then, the gear mesh stiffness is used as the main judgment basis for gear failure. The gear failure model and the system torsion dynamics model are established. The gear meshing stiffness curve under the crack fault is fitted. Finally, the transmission system overall dynamic model is proposed to obtain the motor current and gear fault information. The influence of different faults on the system electromechanical performance is studied. The mapping relationship between the crack fault and the current is established. The typical characteristics under fault conditions are extracted, which is an important reference for studying the health status of the cutting drive system in the shearer.

Fault Detection in Gears Using Fault Samples Enlarged by a Combination of Numerical Simulation and a Generative Adversarial Network

It is inevitable for gear to become damaged, which has a profound effect on the performance of gear transmission systems. Solving the problem of gear fault detection using artificial intelligence models depends on sufficient fault samples, though they might not always exist. A new method using numerical simulation and a generative adversarial network (GAN) is proposed to enlarge fault samples for detecting faults in gears. First, to supplement the missing fault samples, numerical simulation is employed to obtain simulation fault samples. Then, simulation and measurement fault samples are input into the GAN to generate synthetic fault samples to enlarge the training samples. Finally, the simulation, measurement and related synthetic fault samples serve as typical classifiers, including convolutional neural network, recurrent neural network, and stacked autoencoder, while the test samples of unknown faults are finally detected. Three experimental groups are designed to classify gear faults. The average classification accuracy is 100, 98.83, and 97.64%, which confirms the feasibility and effectiveness of the method for detecting gear faults using incomplete fault samples. The idea presented herein is expected to apply in any type of mechanical system that has the corresponding well-constructed numerical simulation model.

Gear Fault Diagnosis Based on Variational Modal Decomposition and Wide+Narrow Visual Field Neural Networks

In modern industrial production, rotating machinery plays an important role. The gears in this machinery adjust the speed and transmission of torque. Therefore, when the gear fails, it is very important to be able to diagnose the fault quickly and accurately. Gear vibration signals are often used in gear fault diagnosis, but the fault signal is often overwhelmed by noises. To enable the scientific and efficient detection of faults, this study proposes a gear fault diagnosis method based on variational modal decomposition (VMD) and wide+narrow visual field neural networks (WNVNNs), namely VMD-WNVNN. VMD-WNVNN consists of two stages. In the feature extraction stage, VMD and Pearson correlation coefficients are used to decompose and reconstruct the original data to obtain the features of these data in the frequency domain. In the classification stage, WNVNN is used to classify the data based on features. The final results of the gear fault diagnosis experiments show that this method not only has higher classification accuracy but also has higher classification stability than other recently proposed methods. Note to Practitioners—The gearbox is composed of many mechanical parts, such as gears, shafts, and bearings. Therefore, the vibration signal collected by the vibration sensor on the gearbox housing will contain the vibration signal of each part and the noise caused by processing errors. Therefore, if some methods can be used to efficiently extract the characteristic signals required for diagnosis in the data processing stage, the efficiency of fault diagnosis will be greatly improved. This article takes the health of gears as the research object and proposes a method that combines adaptive signal decomposition and deep learning technology. Experimental results show that this method has higher classification accuracy and classification stability than other methods proposed recently.

Simulation studies around the steering system of the azimuthing propulsor

In this study, the steering load, the steering control and the gear failure of the steering system of the azimuthing propulsor are simulated and investigated respectively. In this research, CFD simulation was used to obtain the steering load moment for a azimuthing propulsor with different advance coefficients in the azimuthing range of 0∘ to 180∘. Then the simulation results were compared to the open water experiment results to analyze the reasons for the similarities and differences. Finally, the reason for the fluctuations and the fluctuations frequency calculation method were discussed. A co-simulation model of the gear transmission system and the hydraulic control system was built to simulate the steering control process. Due to the limited braking speed in small angle position feedback control, a speed feedback position control method was proposed. Taking into account the influence of fluctuating variable load on the hydraulic system pressure and the dynamic gear meshing force, the optimal control parameters were found, and the final control effect was significantly improved. In view of the pressure change of the hydraulic system contains a wealth of information on gear torsional vibration, the influence of gear failure on the hydraulic system pressure was discussed considering the two gear failure modes of broken teeth and uniform wear. The input load is divided into constant load and fluctuating load, and the test results was consistent which provides a basis for diagnosing azimuth gear faults.

Research on Embedded Ball Screw Diagnosis System for Mechanical Fault Based on Bispectral Image

Ball screw is one of the main basic transmission components in CNC machine tool feed system because of its high transmission efficiency, positioning accuracy, and rigidity. It plays an irreplaceable role in the process of machine tool torque transmission and positioning. However, ball screw is also one of the parts which are easy to break down in the feed system. If it breaks down, it will affect the processing accuracy of the whole machine tool and even cause shutdown. Numerical control means that digital control is a high-efficiency technology that uses digital information to control mechanical movement and processing processes. Therefore, to increase the high-speed and high-precision operation ability of CNC machine tools, research and development of efficient and reliable predictive maintenance and issue identification technology to guide maintenance of CNC machine tools are critical. Throughout this study, a bispectrum image-based embedded defect fight against the disease for cylindrical gears is provided. Firstly, the bispectrum of fault signal is generated by Wavelet Packet Threshold denoising, wavelet packet reconstruction, and bispectrum analysis, and the characteristics of bispectrum are represented by the first kind of gray moments. These feature statistics are used as input feature vectors of BP neural network pattern recognition algorithm to classify these feature sets and identify the corresponding gear fault types. Experiments show that under the condition of noise, after 14.5 hours of testing, the NGI has increased significantly and can be used to diagnose faults in the gear system, and the method achieves relatively ideal recognition rate and verifies the feasibility of gear box fault diagnosis method based on image recognition. The code compiled by OpenCV library is easy to transplant to embedded system, with high development efficiency and reliable program operation. OpenCV has the characteristics of powerful matrix computing capability and convenient and flexible interface, and it is a general algorithm that can realize real-time image processing.

Gear fault detection using noise analysis and machine learning algorithm with YAMNet pretrained network

Gears are an essential machine element in every machinery. Faults in gears must be detected promptly to reduce downtime and avoid the sudden failure of the machine. Intelligent fault diagnosis techniques are gaining impetus due to Industry 4.0. This work proposes to use the YAMNet network for fault detection of gear pairs using transfer learning. YAMNet is a pre-trained network trained on audio data for sound event detection. The knowledge of the pretrained network is transferred to the fault diagnosis model. Research in this area suggests that noise and vibration data in the time-frequency domain is used to train pre-trained networks for fault detection. However, the research on applying pre-trained audio networks is minimal. An experimental setup is fabricated with two identical gear pair sub-assemblies, one in healthy condition and another with one gear tooth failure condition. Noise data is collected and preprocessed to extract Mel Spectrogram and is given at the input size requirement of the YAMNet pretrained network. The final connected layer is replaced, and hyperparameters are fine-tuned to improve accuracy. The final prediction accuracy is 95% for the random test dataset.

Improved vibration AM-FM sideband phenomenon models of planetary gear set with distributed faults and fault-induced speed fluctuation

Vibration modulation features are the important indicators for the planetary gear set fault diagnosis. However, the modulation sidebands caused by the distributed faults of planetary gear set are complicated, especially the FM components related to fault-induced periodic speed fluctuation, which have not been clearly revealed. In this paper, the transfer path function, gear meshing force and measuring-direction projection function with FM components are combined to develop a series of phenomenon models when considering the distributed gear fault-induced speed fluctuation, which can theoretically deduce the vibration modulation sidebands. Field test of the wind turbine planetary gearbox and the fault simulation of experiment platform verify the high consistency of the vibration AM-FM sidebands obtained from the phenomenon models and the practical planetary gear sets. The proposed models contribute a clear explanation for the generation mechanism of vibration AM-FM sidebands even the sidebands with small amplitudes caused by the distributed faults of sun and planet gear, and provide the important and effective frequency features to confirm the condition of planetary gear set.

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations

Fault detection and diagnosis of gear systems using vibration measurements play an important role in ensuring their functional reliability and safety. Computational intelligence, leveraging upon classification through various surrogate models, has recently demonstrated certain level of success. Major challenge however remains. The establishment of surrogate models generally requires large size of training data with specific labels corresponding to explicitly known gear fault conditions, which may not be available in practical applications. Both the size of available data and the respective labels may be quite limited due to the high cost, which hinders the diagnosis of unseen/unexpected faults with desired reliability. In this research we synthesize a deep convolutional generative adversarial network (DCGAN) to tackle this challenge. This new approach follows the semi-supervised learning concept, the performance of which is significantly enhanced by introducing additionally the inexpensive unlabeled data. The balanced adversarial effect between the discriminator and generator in DCGAN is realized by appropriately designing their architectures, which as a result can enable the high accuracy of diagnosis with scarce labeled data. More importantly, by taking full advantage of the rich fault signatures in the unlabeled data that point to the diverse unseen faults, the intrinsic correlation of underlying physics between the unseen and known faults can be implicitly elucidated via unique semi-supervised learning strategy featured in DCGAN. Therefore, the extended capability in diagnosing the unseen faults that are beyond the known faults in training dataset can be realized, which bears practical significance. Systematic case studies using experimental data acquired from a lab-scale gear system are carried out to validate the new diagnosis framework.

Triboelectric sensor for planetary gear fault diagnosis using data enhancement and CNN

The planetary gear transmission system is characterized by a complex structure and is often exposed to harsh working conditions, resulting in high failure damage rate. The self-powered sensing system based on the triboelectric nanogenerator provides an ideal scheme for online monitoring of the planetary gear operating condition. In this paper, a triboelectric planetary gear sensor (TPGS) is proposed for real-time monitoring of the speed and gear faults of the planetary reducer. The sensor is integrated at the tooth crest and root meshing gap of the planetary gear and the ring gear, which ensures the integrity of the gear structure and function. The results of the sensing experiment show that TPGS can be used to detect the speed of the planetary reducer in the range of 10–1000 rpm. Moreover, the speed detection error rate is less than 0.4%. To identify the gear faults, the stacked autoencoder (SAE) is used to enhance the data collected by TPGS, combined with convolutional neural network (CNN). The results show that the classification accuracy reached 100%. In this paper, a theoretical and experimental basis for development and online fault monitoring of a new intelligent gearbox with self-awareness and self-regulation is provided.

Numerical simulation of gears for fault detection using artificial intelligence models

Artificial intelligence (AI) models are widely used in every field of life to classify and predict the abnormal conditions. The key to activating AI models is to obtain enough training samples. The lumped parameter model is the one of the best choices to provide sufficient samples. In this study, the lumped parameter model is used in the numerical simulation of gears to obtain enough training samples to detect their running state. The aim is to replace the traditional sample generation method with the modified lumped parameter numerical method to realize the fault diagnosis of gears. Firstly, the lumped parameter model of gears is constructed and further updated. Secondly, failure types are inserted into the updated lumped parameter model and generate samples. The set composed of measured signals and simulated signals is equally divided, and the corresponding time-domain signal indexes are calculated to form a sample matrix for AI training and classification. Finally, CNN, RNN and LSTM are selected as the representatives of AI model, and then the trained models are used to classify the unknown measurement fault samples. The classification model trained by the simulation fault samples has almost 100% accuracy for the current gear fault classification, which shows that it is a feasible way to generate sample data for artificial intelligence fault classification by using the lumped parameter models.

Rinser Machine and the Dynamics-based Failure Analysis for Spur Gear Fault Detection: A Forensic Review

Gear fault detection and its severity is essential for analyzing the availability, maintainability and reliability of rinser machines. The occurrence of multiple failures of gears and their analysis is becoming more challenging, thereby causing the increased unavailability of rinser machines. This study reviewed the rinser machine and its various advantages and disadvantages, including the problems associated with the machine. More so, various types of failures associated with the spur gears were studied, and it was established that the detection and the diagnosis of gear faults would inform a maintenance planner on scheduled maintenance. Thus, the absence of failure detection will cause uncontrolled machine breakdown and, consequently, result in unsafe operation as well as a loss of productivity. The study concludes and recommends grey cast iron as alternative material that could reduce chemical failure and failure resulting from fatigue.

Ramanujan Fourier Mode Decomposition and Its Application in Gear Fault Diagnosis

As an important part of rotating machinery, gear is easy to appear some unexpected fault states, and its fault diagnosis is very important. Fourier decomposition method (FDM) is a common method for gear fault diagnosis, but the noise robustness, period recognition, and extraction capabilities of FDM are unsatisfactory. Based on this, in this article, Ramanujan Fourier mode decomposition (RFMD) method is proposed. The RFMD not only has a complete mathematical theory foundation but also has an excellent ability to identify and extract periodic components. Emulational and experimental results of planetary gearbox show that the RFMD method has good noise robustness and can accurately extract gear fault characteristic information. Thus, it is an effective gear fault diagnosis method.

Removal of transfer function effects from gear vibration signals under constant and variable speed conditions

Gear faults generally cause changes to the forcing functions of the system, which are usually hard to measure. Vibration is a common tool for gear condition monitoring, it being generated by a forcing function exciting responses through a transmission path from the excitation to the measurement point. The diagnostic information in the vibration measurements is often distorted by transfer function effects, in particular for local faults, such as cracks, where the waveform is important. In such cases, more direct diagnostic information can be extracted by removing the transfer function effects. However, a very limited amount of research has been carried out to reconstruct the time-domain waveforms of the forcing functions, except some making the assumption that the forcing functions are impulsive. A method was previously developed to identify and remove the transfer function effects from transmission error (TE) measurements at constant speeds to reconstruct the excitations, utilising a cepstrum-based operational modal analysis (OMA) method. In the current paper, this method is extended to vibration signals under constant and variable speed conditions. Since vibration has more complex transfer functions than TE, this paper establishes an additional pre-processing procedure, which removes the deterministic components in order to facilitate the OMA process. After removing the transfer function effects from measured vibration signals, the proposed technique reconstructs forcing functions that are much more clearly indicative of the faults at all speeds and which approximate the meshing forces at the gear teeth. The fault features are effectively enhanced and localised in the forcing functions, and the importance of phase modification to the reconstruction of forcing function waveforms is demonstrated. The technique was validated using vibration measurements obtained from a single stage spur gear box with a tooth root crack.

Gear Fault Diagnosis and Life Prediction of Petroleum Drilling Equipment Based on SOM Neural Network.

In order to solve the problem that variable working conditions and fault types cannot be diagnosed in gear fault diagnosis of petroleum drilling equipment, four kinds of faults, namely, gear broken tooth, gear crack, gear pitting, and gear wear, are studied in this paper. Based on the SOM neural network algorithm, an intelligent diagnosis model of gear fault is proposed, and the PCA method is used to reduce data dimension and fuse features. The state index of life prediction is determined, and the remaining service life prediction of gear transmission system is predicted based on exponential degradation model. The results show that the accuracy of the SOM model for fault diagnosis is high, and the bearing in gearbox can be replaced or repaired in advance according to the residual life curve, so as to achieve the purpose of predictive maintenance.

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

In multi-stage gear transmission system, the accident is often not caused by a single fault, but by coupling fault. The decoupling and identification of coupling faults has always been a hot topic. Through the analysis of frequency characteristics and transmission characteristics, this paper explored the correlation of coupling faults in multistage gear transmission system, and analyzed the coupling mechanism of faults. According to the fault test-bed, a 3D model was built to analyze the contact force and vibration characteristics of gears at all stages. STFT and waterfall diagram were used to analyze the coupling correlation characteristics of experimental signals under two single faults and coupling faults: fixed-axis gear crack and planetary gear wear. The contact force transfer function of all stages of gears to the box was built by using the system identification theory. The source of the fault characteristic frequency component was found through the transfer function, and the transmission characteristic of the fault was revealed. It can be seen from the results that the fault coupling is not the superposition of frequency characteristics. In the coupled signal, the frequency characteristic with the largest amplitude increase is not the fault gear itself, but the intermediate gear connected with them and affected by them. This phenomenon is often misled and misjudged in the analysis.

Evaluation of gear pitting severity by using various condition monitoring indicators

Fault detection techniques based on vibration measurement are implemented to identify in an early stage failures appearing in gear transmissions. Condition monitoring indicators (CMI), like: Root Mean Square (RMS), Crest Factor, Kurtosis, FMO, FM4, Energy ratio, Energy operator, NA4 or NB4, are used to estimate the level of gear faults such as pitting, cracks, spalling, scuffing or scoring. However, in is multitude of indicators, the question that arises is: which CMI is the most sensitive in estimating the severity of defects? Thus, this paper presents an extensive comparison between the before mentioned indicators computed from vibration signals collected on four pinions with different pitting grades, created by artificial means. The pinions where incorporated in a single helical gearbox and the tests were performed on an open-energy test rig at three different input speeds. This comparative study assesses the receptivity of different condition monitoring indicators towards gear pitting failure.

A Study on Gear Defect Detection via Frequency Analysis Based on DNN

In this paper, we introduce a gear defect detection system using frequency analysis based on deep learning. The existing defect diagnosis systems using acoustic analysis use spectrogram, scalogram, and MFCC (Mel-Frequency Cepstral Coefficient) images as inputs to the convolutional neural network (CNN) model to diagnose defects. However, using visualized acoustic data as input to the CNN models requires a lot of computation time. Although computing power has improved, there is a situation in which a processor with low performance is used for reasons such as cost-effectiveness. In this paper, only the sums of frequency bands are used as input to the deep neural network (DNN) model to diagnose the gear fault. This system diagnoses the defects using only a few specific frequency bands, so it ignores unnecessary data and does not require high performance when diagnosing defects because it uses a relatively simple deep learning model for classification. We evaluate the performance of the proposed system through experiments and verify that real-time diagnosis of gears is possible compared to the CNN model. The result showed 95.5% accuracy for 1000 test data, and it took 18.48 ms, so that verified the capability of real-time diagnosis in a low-spec environment. The proposed system is expected to be effectively used to diagnose defects in various sound-based facilities at a low cost.