Discrete wavelet transform and artificial neural network for gearbox fault detection based on acoustic signals

Gearboxes are widely applied in power transmission lines, so their health monitoring has a great impact in industrial applications. In the present study, acoustic signals of Pride gearbox in different conditions, namely, healthy, worn first gear and broken second gear are collected by a microphone. Discrete wavelet transform (DWT) is applied to process the signals. Decomposition is made using Daubichies-5 wavelet with five levels. In order to identify the various conditions of the gearbox, artificial neural network (ANN) is used in decision-making stage. The results indicate that this method allow identification at a 90 % level of efficiency. Therefore, the proposed approach can be reliably applied to gearbox fault detection

Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.Comment: Under review / added arXiv identifie

Physicochemical, antioxidant and sensory properties of Mango Sorbet containing L-theanine as a potential functional food product

The non-proteinous amino acid L-theanine (L-THE) is associated with a range of health benefits including improvements in immune function, cardiovascular outcomes and cognition. The aims of this study were to develop a food product (mango sorbet; ms-L-THE) containing physiologically relevant doses of L-THE (0.2/100 g w/w) and determine its antioxidant, physicochemical and sensory properties in comparison to a mango sorbet without L-THE (ms). Total phenolic and flavanol content, and antioxidant analysis (DPPH, FRAP and ABTS) were determined spectrophotometrically. Both products were also evaluated for acceptability and likeability in healthy participants using the 9-point hedonic scale. Any differences that could be caused by the addition of L-THE were examined using the triangle test. Results indicated no significant differences between ms-L-THE and ms in taste of the products (p > 0.05), and the ms-L-THE was well received and accepted as a potential commercial product. Findings of the DPPH assay indicated significant difference between the two products (p < 0.05). In conclusion, we have successfully created a mango sorbet that contains a potentially physiologically relevant concentration of L-THE with antioxidant properties that could be used as a novel method of L-THE delivery to clinical and healthy populations

Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three time-frequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time

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

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries