A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults

Investigation of a multi-sensor data fusion technique for the fault diagnosis of gearboxes

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordGearbox is the key functional unit in a mechanical transmission system. As its operating condition being complex and the interference transmitting from diverse paths, the vibration signals collected from an individual sensor may not provide a fully accurate description on the health condition of a gearbox. For this reason, a new method for fault diagnosis of gearboxes based on multi-sensor data fusion is presented in this paper. There are three main steps in this method. First, prior to feature extraction, two signal processing methods, i.e. the energy operator and time synchronous averaging, are applied to multi-sensor vibration signals to remove interference and highlight fault characteristic information, then the statistical features are extracted from both the raw and preprocessed signals to form an original feature set. Second, a coupled feature selection scheme combining the distance evaluation technique and max-relevance and min-redundancy is carried out to obtain an optimal feature set. Finally, the deep belief network, a novel intelligent diagnosis method with a deep architecture, is applied to identify different gearbox health conditions. As the multi-sensor data fusion technique is utilized to provide sufficient and complementary information for fault diagnosis, this method holds the potential to overcome the shortcomings from an individual sensor that may not accurately describe the health conditions of gearboxes. Ten different gearbox health conditions are simulated to validate the performance of the proposed method. The results confirm the superiority of the proposed method in gearbox fault diagnosis.National Natural Science Foundation of Chin

Vibration-based Fault Diagnostics in Wind Turbine Gearboxes Using Machine Learning

A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal components unrelated to faults and high levels of noise obscure the signature generated by early-stage damage. To address this issue, we propose an innovative fault diagnosis framework that utilizes deep learning and leverages cyclostationary analysis of sensor data. By generating cyclic spectral coherence maps from the sensor data, we can emphasize fault-related signatures. These 2D color map representations are then used to train convolutional neural networks capable of detecting even minor faults and early-stage damages. The proposed method is evaluated using test data obtained from multibody dynamic simulations conducted under various operating conditions. The benchmark test cases, inspired by an NREL study, are successfully detected using our approach. To further enhance the accuracy of the model, subsequent studies employ Convolutional Neural Networks with Local Interpretable Model-Agnostic Explanations (LIME). This approach aids in interpreting classifier predictions and developing an interpretable classifier by focusing on a subset range of cyclic spectral coherence maps that carry the unique fault signatures. This improvement contributes to better accuracy, especially in scenarios involving multiple faults in the gearbox that need to be identified. Moreover, to address the challenge of applying this framework in practical settings, where standard deep learning techniques tend to provide inaccurate predictions for unseen faults or unusual operating conditions, we investigate fault diagnostics using a Bayesian convolutional neural network. This approach incorporates uncertainty bounds into prediction results, reducing overconfident misclassifications. The results demonstrate the effectiveness of the Bayesian approach in fault diagnosis, offering valuable implications for condition monitoring in other rotating machinery applications

Analysis of the efficiency of wind turbine gearboxes using the temperature variable

The aim of this paper is to evaluate how lubricant selection affects gearbox efficiency and overall energy production by analysing real data from wind farms, monitored and controlled by a Supervisory Control and Data Acquisition (SCADA system). The turbines analysed worked with two or more oil types for the same amount of hours, which allowed to establish relations between the active power curves and wind velocity; oil temperature inside gearboxes and wind velocity; and oil temperature inside gearboxes and active power production. The results of this study evidenced a direct relation between oil characteristics and energy efficiency i.e. gearboxes working with mineral oil perform better then gearboxes working with synthetic oils. Those differences can be significant in terms of active power production. Also, it was observed oil degradation as function of temperature increase, with changes on viscosity, which reveals that temperature behaviour along the active power curve is strongly related to oil' characteristics. (C) 2018 Elsevier Ltd. All rights reserved.Agência financiadora Portuguese Foundation for Science and Technology (FCT) PTDC/AAG-TEC/1710/2014 MONITOR project - Atlantic Area EAPA_333/2016 Portuguese Foundation for Science and Technology under the Portuguese Researchers' Programme 2014 IF/00286/2014/CP1234 Marie Sklodowska-Curie Actions of the European Union's H2020-MSCA-IF-EF-RI-2016/under REA - 748747info:eu-repo/semantics/publishedVersionhttp://creativecommons.org/licenses/by/4.0

A Machine Learning Approach for Gearbox System Fault Diagnosis

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter's prediction error. The obtained prediction error's standard deviation is further processed with a support-vector machine to classify the gearbox's condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings

Vibration condition monitoring of planetary gears based on decision level data fusion using Dempster-Shafer theory of evidence

In recent years, due to increasing requirement for reliability of industrial machines, fault diagnosis using data fusion methods has become widely applied. To recognize crucial faults of mechanical systems with high confidence, indubitably decision level fusion techniques are the foremost procedure among other data fusion methods. Therefore, in this paper in order to improve the fault diagnosis accuracy of planetary gearbox, we proposed a representative data fusion approach which exploits Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers and Dempster-Shafer (D-S) evidence theory for classifier fusion. We assumed the SVM and ANN classifiers as fault diagnosis subsystems as well. Then output values of the subsystems were regarded as input values of decision fusion level module. First, vibration signals of a planetary gearbox were captured for four different conditions of gear. Obtained signals were transmitted from time domain to time-frequency domain using wavelet transform. In next step, some statistical features of time-frequency domain signals were extracted which were used as classifiers input. The gained results of every fault diagnosis subsystem were considered as basic probability assignment (BPA) of D-S evidence theory. Classification accuracy for the SVM and ANN subsystems was determined as 80.5 % and 74.6 % respectively. Then, by using the D-S theory rules for classifier fusion, ultimate fault diagnosis accuracy was gained as 94.8 %. Results show that proposed method for vibration condition monitoring of planetary gearbox based on D-S theory provided a much better accuracy. Furthermore, an increase of more than 14 % accuracy demonstrates the strength of D-S theory method in decision fusion level fault diagnosis

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.Peer ReviewedPostprint (published version