Gearbox Failure Research Articles

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG.

The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

Integrated dynamic analysis of a spar floating wind turbine with a hydraulic drivetrain

Conventional drivetrains with gearbox failures are associated with major downtime in offshore wind turbines. This impacts the maintenance cost and cost of energy. A hydraulic transmission eliminates the need for gearbox and potentially improves the turbine reliability. This paper explores the application of a novel high-pressure transmission machinery to a utility-scale spar floating wind turbine. We present a dynamic model of a hydraulic system consisting of a hydraulic pump, pipelines, a hydraulic motor, and an induction generator. The motor is placed inside the spar platform and operates at a fixed displacement. The hydraulic system is coupled with the aero-hydro-elastic code HAWC2 through an external dynamic link library. The dynamic responses of a 5 MW spar floating wind turbine and a land-based reference wind turbine with this hydraulic system were investigated under various wind and wave conditions. A comparison is made between the characteristics of the two wind turbines under operational conditions and both turbines show decent behaviors, and the statistics of power generation is at the same level compared to that of conventional wind turbines. Through simulation studies, we verify the feasibility and the engineering practice of the proposed hydraulic drive solution to floating wind turbines.

Design and Analysis of Permanent-Magnet Vernier Machine for Direct-Driven Wind Power Generator Considering Pole-Slot Combinations

Wind power turbines have increasingly attracted attention as a renewable energy source. Gearboxes in wind turbines reduce their efficiency, require periodic maintenance, and result in gearbox failure. Consequently, direct-driven generators with a high torque density have emerged as an alternative because they transmit the high torque of the wind turbine directly. Permanent-magnet vernier machines (PMVMs) possess a simple and robust structure and significantly high torque density at low speeds. Although various topologies and pole-slot combinations of PMVMs have been considered to enhance their performance, studies on fractional-slot distributed-winding PMVMs (FSDW PMVMs) remain limited compared with those on fractional-slot concentrated-winding PMVMs (FSCW PMVMs). In this study, we designed eight models, including permanent magnet synchronous machines (PMSMs), FSDW, and FSCW PMVMs for direct-driven wind power generators. Their performances in terms of torque density, torque ripple, losses, and power factor were compared and verified using finite element analysis. The FSDW PMVM was proposed as the best model for direct-driven wind power generators based on the torque density, torque ripple, and possibility of performance improvement.

A Continual Learning-Based Framework for Developing a Single Wind Turbine Cybertwin Adaptively Serving Multiple Modeling Tasks

This article proposes a generalized neural continual learning-based cybertwin (GNC) modeling framework to realize developing one wind turbine (WT) cybertwin serving multiple modeling tasks in the wind farm operations and maintenance (O&M). A generalized WT cybertwin modeling problem, which considers modeling one cybertwin for multiple tasks without additional computational burden, is studied for the first time. Fully connected neural networks are adopted as the backbone for developing the GNC model. The online elastic weight consolidation method is incorporated to mitigate the catastrophic forgetting phenomenon among different modeling tasks. Computational experiments are conducted to validate the effectiveness of the proposed GNC framework based on the supervisory control and data acquisition data. Modeling tasks in three important problems of the wind farm O&M, the WT gearbox failure detection, WT blade breakage detection, and wind power prediction, are considered in the experiment. Compared with other benchmarking models, such as the multiple neural cybertwins, neural cybertwin, and regularized neural cybertwin, the proposed GNC can achieve high accuracies on both new tasks and existing tasks, which further verifies the WT cybertwin generalization via the proposed GNC.

Unbalance Failure Recognition Using Recurrent Neural Network

Many machine learning models have been created in recent years, which focus on recognising bearings and gearboxes with less attention on detecting unbalance issues. Unbalance is a fundamental issue that frequently occurs in deteriorating machinery, which requires checking prior to significant faults such as bearing and gearbox failures. Unbalance will propagate unless correction happens, causing damage to neighbouring components, such as bearings and mechanical seals. Because recurrent neural networks are well-known for their performance with sequential data, in this study, RNN is proposed to be developed using only two statistical moments known as the crest factor and kurtosis, with the goal of producing an RNN capable of producing better unbalanced fault predictions than existing machine learning models. The results reveal that RNN prediction efficacies are dependent on how the input data is prepared, with separate datasets of unbalanced data producing more accurate predictions than bulk datasets and combined datasets. This study shows that if the dataset is prepared in a specific way, RNN has a stronger prediction capability, and a future study will explore a new parameter to be fused along with present statistical moments to increase RNN’s prediction capability.

Adaptation Regularization Based on Transfer Learning for Fault Diagnosis of Rotating Machinery Under Multiple Operating Conditions

Greater difference in data distribution of rotating machinery under multiple operating conditions, which increases the difficulty of fault diagnosis. To solve this problem, a multi-condition fault diagnosis method for rotating machinery using adaptation regularization based on transfer learning (ARTL) was proposed. First, a base classifier is trained in the source domain to predict pseudo-labels of the target domain, and the two-domain data distribution is adapted by joint distribution to reduce the difference of data distribution. Then, the local neighborhood relationship of the samples is learned through manifold regularization, and the local geometric characteristics of multi-condition data are mined. Finally, following the framework of structural risk minimization (SRM), the kernel function is selected to build the classifier, which iteratively updates the pseudo-labels in the target domain and obtains the coefficient matrix to complete the fault diagnosis of rotating machinery. Experimental results on bearing and gearbox failure datasets show that the method has good feasibility and effectiveness under multiple operating conditions.

Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data

The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.

Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

The performance and efficiency of a baler deteriorate as a result of gearbox failure. One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes. This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox. This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction. The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information. Therefore, this method should be capable of timely diagnosis and monitoring. Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained. The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information, 100% fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods. The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.

Condition-Based Monitoring on High-Precision Gearbox for Robotic Applications

This work presents a theoretical and experimental study regarding defect detection in a robotic gearbox using vibration signals in both cyclostationary and noncyclostationary conditions. The existing work focuses on inferring the health of the robot during operation with little regard toward the defective element of the components. This article illustrates the detection of specific element damage of a robotic gearbox during a robotic cycle based on domain knowledge and presents a novel data-driven method for asset health. This starts by studying the robotic gearbox, specifically its kinematics as a planetary 2-stage reduction gearbox to acquire the knowledge of the rotations of each component. The signals acquired from a test bench with four sensors undergo different acquisition methods and signal processing techniques to correlate the elements’ frequencies. The work shows the detection of the artificially created defects from the acquired vibration data, verifying the kinematic methodology and identifying the root cause of failure of such gearboxes. A novel resampling method, Binning, is presented and compared with the traditional signal processing techniques. Binning combined with Principal Component Analysis (PCA) as a data-driven method to infer the state of the gearbox is presented, tested, and validated. This work presents methods as a step toward automatized predictive maintenance on robots in industrial applications.

A novel cyclic-correntropy based indicator for gear wear monitoring

Gearbox is a vulnerable component of a turbine’s drivetrain and plays a vital role in the power transmission in wind turbines. Wind turbines usually operate under harsh working environments, such as in deserts, oceans, and on hills. The adverse operating conditions (such as inevitable fluctuating wind loads and speeds) make the gearbox transmission prone to reliability degradation and premature failure. Gear wear is a common and unavoidable surface degradation phenomenon during the lifespan of the gear transmission system. The gear wear propagation can result in severe failures, such as gear surface spalling, gear root crack, and gear tooth breakage, all of which could lead to the failure of the drivetrain system of wind turbines and bring unexpected economic loss, even serious accidents. Thus, it is crucial to monitor the gear wear propagation progression in order to enable reliable and safe operation. To this end, this paper develops a novel vibration-based health indicator to monitor the gear surface degradation induced by gear wear progression. With the help of the novel indicator developed, the health status of the gearbox can be well evaluated and thus predictive maintenance-based decisions can be made to reduce maintenance costs and minimize gearbox failures in wind turbines. A series of endurance tests under different lubrication conditions and operational conditions are carried out to verify the effectiveness of the gear wear monitoring indicator.

AutoML for Feature Selection and Model Tuning Applied to Fault Severity Diagnosis in Spur Gearboxes

Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.

ПРОЕКТНЫЙ РАСЧЕТ ЗУБЧАТОЙ ПЕРЕДАЧИ ГРУЗОВОГО ЭЛЕКТРОВОЗА С ОПТИМАЛЬНЫМИ ПАРАМЕТРАМИ КОРРИГИРОВАНИЯ

In result of the study the authors have established that mistakes in design a gearwheel are among main reasons that lead to a failure of a gearwheel for the 2ES6 freight electric locomotive. In order to increase life of the gearwheel they suggest a method for its redesign with adjustment of offset coefficients. The authors have developed a cylindrical involute helical gearwheel. With the use of modeling in the «Shafts and mechanical gears» application within the framework of the KOMPAS-3D system the authors have got strength and geometrical parameters of the redesigned gearwheel that meets all reliability requirements. Moreover, they have carried out a durability calculation that has confirmed correctness of gained values. As a result, the use of the developed model with optimal parameters of correlation adjustment will contribute to a decrease of a number of traction gearbox failures.

From Anomaly Detection to Novel Fault Discrimination for Wind Turbine Gearboxes With a Sparse Isolation Encoding Forest

As vital renewable energy devices, wind turbines suffer from gearbox failures due to harsh speed increasing operations. Therefore, the gearbox fault diagnosis is crucial for wind turbine maintenance with reducing economic costs. However, obtaining faulty data is rather challenging, especially at the early fault stage. For this reason, a sparse isolation encoding forest (SIEF) is proposed aiming at both anomaly detection and novel fault discrimination for wind turbine gearboxes. In the present SIEF method, a sparse autoencoder is first trained with only normal data to obtain an optimized and robust weight structure. Newly acquired data corresponding to faulty or healthy conditions are sent to this encoder for feature extraction by encoding to its low dimensional space. All the data in low-dimensional space are fed to an isolation forest for anomaly detection and novel fault discrimination. In the addressed SIEF approach, only normal data are required to train the model for fault detection and further discrimination. It is consistent with the actual operations of the wind turbines, with much less dependence on the fault data for the model training. The proposed method was evaluated by fault diagnosis tests on the wind turbine gearboxes. Results show good performances of the proposal compared to peers.

Increasing life of pit dump truck motor-wheel gearboxes by introducing the monitoring of the actual technical condition

At present, Kuzbass coal strip mines pay great attention to improving quality performance of mining equipment operation, including reliability and durability of components and units. One of the ways of the performance improvement is decreasing number of unforeseen failures. To achieve this purpose a mine dump truck part diagnostics should be introduced into a maintenance service procedure. At the same time the process of diagnostics should not increase the machinery downtime, but effectively reveal a condition of motor-wheel gearboxes in the course of dump truck operation. The aim of the research is to increase the reliability and service life of motor-wheel gearboxes of large BelAZ dump trucks. Failure of a motor-wheel gearbox is a rare phenomenon, but the cost of a new gearbox can vary from 3.5 to 10 million rubles. That is why it is important to implement such methods of diagnostics, which allow revealing the condition of gearboxes in the shortest possible time and without disassembling corresponding units. Determination of the actual technical condition of motor-wheel gearboxes is possible by different methods: vibroacoustic; acoustic; thermal; physical and chemical analysis of spent operating materials. The studies showed that none of these methods can be used as a universal one. When justifying and selecting the most suitable method, different factors should be considered, including technological, or a combination of methods should be applied, which will reduce risks, but at the same time increase costs. It is necessary to develop a better diagnostic method based on the use of several methods simultaneously.

Анализ проблем механических редукторов транспортных систем и их решение с помощью магнитного датчика

Objective: Increasing reliability of mechanical gearboxes using a magnetic sensor, which will sig nifi cantly increase the reliability of transport mechanisms and systems in general. Methods: An analysis of incidents covered in the media was carried out. Results: Conclusions are drawn about the consequences of failure of mechanical gearboxes. To prevent emergency situations caused by a breakdown of a mechanical gearbox, it is proposed to use a magnetic sensor, which allows carrying out analysis and diagnostics of the gearbox and its components during operation. A mathematical model of a magnetic sensor is compiled and a block diagram of a device for processing the output signal of the sensor modulator is presented. Practical importance: Failure of a mechanical gearbox is a common problem that leads to a stop of transport mechanisms and systems. Detection of a mechanical gearbox malfunction can signifi cantly reduce the cost of repairs, as well as prevent unexpected damage to the mechanism, which can lead to a complete stop of the transport system. The use of a magnetic sensor in the traction gearbox of an electric train will reduce the likelihood of a traction gearbox breakdown during operation and reduce the cost of repairs, increase the reliability of the traction drive, and also simplify the commissioning of new electric trains and traction gearboxes.

Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments

Fault diagnosis has a great significance in preventing serious failures of rotating machinery and avoiding huge economic losses. The performance of the existing fault diagnosis approaches might be affected by two factors, i.e., the quality of fault features extracted from monitoring signals and the capability of fault diagnosis model. This paper proposes a new fault diagnosis method combined mel-frequency cepstral coefficients (MFCC) with a designed parallel multi-fusion convolutional neural network (MFCNN) Specifically, a MFCC-based feature extraction method is defined to reduce the noise components in monitoring signal of rotating machinery and extract more useful low-frequency fault information for downstream task. Furthermore, a novel MFCNN is designed to enrich the high-level features after each convolution operation by using multiple activation functions, so as to improve the quality of the obtained fault features. Meanwhile, a new parallel MFCNN is constructed by using a defined structural ensemble operation to improve its diagnostic performance in different noise environments. Two typical bearing and gearbox failure datasets are applied to evaluate the performance of the proposed fault diagnosis method. The experimental results indicate that the proposed parallel MFCNN has the better diagnostic performance than other methods.

Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.

Maximizing the total power generation of faulty wind turbines via reduced power operation

The ever-increasing energy demand makes wind power more and more popular each passing day. However, due to long-term work in a complex environment, wind turbines are prone to failures. When a failure occurs, wind turbines are directly shut down for maintenance. However, this is not always needed since the turbines can continue operating by reducing their power output and hence improve the total power generation. In this study, continuous operation time is calculated by analyzing the status of each operating state parameter after a failure. Furthermore, a method of reducing power and increasing the continuous operation time of faulty wind turbines is proposed. The feasibility of this method is analyzed through three common wind turbine gearbox failures. Then, the relationships between continuous operation time and reduced power are studied. Finally, the total power generation and the maintenance cost for different power reduction rates are analyzed. The results show that the proposed method can extend the continuous operation time of wind turbines and increase the total generation after a gearbox failure.

Noninvasive Expandable Endoprostheses From Reconstruction to Skeletal Maturity: A Surgical Technique and Lengthening Guide.

Limb salvage is the benchmark for pediatric extremity bone sarcomas. However, reconstructive strategies must account for any anticipated remaining growth potential and the resultant limb inequality. Expandable endoprostheses offer the theoretical advantage of immediate weight-bearing, predictable function, and reliable maintenance of leg-length equality. The evolution of the lengthening mechanism now permits noninvasive lengthening, opposed to the multiple open procedures of the past. These design improvements have contributed to their growing popularity. Experience has indicated that these noninvasive implants more reliably achieve leg-length equality, have longer failure-free survival, and decreased complications, although some have noted gearbox and lengthening failures. Currently, no standardize technique exists for managing patients with noninvasive expandable implants from the time of reconstruction to final lengthening at skeletal maturity. This blueprint aims to provide a detailed surgical technique, lengthening schedule, and recommendations for the mitigation and management of complications to achieve successful limb salvage with noninvasive expandable endoprostheses.

Effects of transmission shaft flexibility on rolling element bearing tribodynamics in a high-performance transmission

In a high-performance transmission, rolling bearings play a crucial role in supporting the loaded shafts and gear mesh whilst minimising friction. Prediction of critical speeds and dynamic responses is important to avoid prolonged periods of elevated vibration amplitudes, which can contribute to premature bearing and gearbox failure. Nonlinearities such as backlash and time-varying stiffnesses can lead to complex coupled dynamics between gears and bearings. In the current study, an 8-speed transmission is discretized and modelled by the finite element method. The eigenproblem is solved using damped modal analysis considering gyroscopic effects. Time-varying stiffness data and backlashes are introduced as nonlinearities. The system response is compared for cases of linear and nonlinear bearings, and rigid and flexible shafts to ascertain the significance of elasticity. The flexible model improves the accuracy of bearing load oscillation by altering the transfer path between gears and bearings. This provides an advancement upon existing works, where shaft dynamics are often simplified. Furthermore, it is shown that the combination of local nonlinear effects and flexible components significantly changes the response of the system and the bearing life prediction for a system-level analysis.