Guotao Meng

Bio: Guotao Meng is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Geology & Geotechnical engineering. The author has an hindex of 1, co-authored 2 publications receiving 195 citations.

Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine

Abstract: In most current intelligent diagnosis methods, fault classifiers of electric machine are built based on complex handcrafted features extractor from raw signals, which depend on prior knowledge and is difficult to implement intelligentization authentically. In addition, the increasingly complicated industrial structures and data make handcrafted features extractors less suited. Convolutional neural network (CNN) provides an efficient method to act on raw signals directly by weight sharing and local connections without feature extractors. However, effective as CNN works on image recognition, it does not work well in industrial applications due to the differences between image and industrial signals. Inspired by the idea of CNN, we develop a novel diagnosis framework based on the characteristics of industrial vibration signals, which is called dislocated time series CNN (DTS-CNN). The DTS-CNN architecture is composed of dislocate layer, convolutional layer, sub-sampling layer and fully connected layer. By adding a dislocate layer, this model can extract the relationship between signals with different intervals in periodic mechanical signals, thereby overcome the weaknesses of traditional CNNs and is more applicable for modern electric machines, especially under nonstationary conditions. Experiments under constant and nonstationary conditions are performed on a machine fault simulator to validate the proposed framework. The results and comparison with respect to the state of the art in the field is illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

Sparse components separation-based operational reliability assessment approach

Abstract: As a metric to quantize the engineering system and plants quality, reliability has developed as a scientific discipline which is mainly rely on statistical analysis and life tests. However, with the improvement of mechanical system quality and service time, access to life tests and historical failure data become more and more difficult and time-consuming. To overcome the dependence of statistical failure data, a novel operational reliability assessment approach is proposed. System vibration response varies from operational states to states. In a bearing-rotor system, the vibration response of failure system is the impulse component. Besides, the vibration caused by abrasion is the harmonic component. For impulse and harmonic components extraction, a morphological component analysis (MCA) method based on basis pursuit denoising (BPDN) is used to decompose the vibration signals and reconstruct the impulse signals. Then classical time domain indexes of impulse signals are used as the observation sequence of a corresponding Hidden Markov Models (HMM) to assessment operational reliability. Finally, BPDN, traditional time-features and the proposed method are respectively applied in the operational reliability assessment of an experiment carried out on an aerospace bearing test rig. Comparison results confirmed the effectiveness of the proposed method for operational reliability assessment in bearing-rotor system.

Numerical Simulation of Water Impoundment at a High Arch Dam Site

Abstract: AbstractIn a former study, water impoundment at a high arch dam (located in southwest China) was simulated using fluid-mechanical elasto-plastic analyses to predict deformation mechanisms taking place at the scale of the valley (Hou et al., IOP Conf Ser: Earth Environ Sci 570:022033, 2020). In this continuation work, we review the findings, clarify the mechanism of valley deformation caused by changes in impoundment level, and present the results of a sensibility study that accounts for the presence of drainage volumes at the site. With no account for drainage volumes, the model predicts valley expansion along measuring lines upstream from the dam, and valley contraction downstream. Also, valley contraction increases with measuring line elevation. Valley deformation occurs during changes of impoundment; this reflects the mechanical response to the change in dead weight of standing impoundment water. Deformation also takes place at constant impoundment level; this is caused by large-scale water seepage under and around the dam in the valley banks. A decrease in impoundment level generates a relative increase in valley contraction in the model, both upstream and downstream of the dam. This behavior is explained by the mechanical relaxation of lateral pressure that acts on the valley banks when the impoundment level is reduced. With drainage domains (associated with tunnels and powerhouse caverns) accounted for in the model, it was expected that the associated drop in fluid pressure would reduce the predicted valley contraction in the dam vicinity. Indeed, in this case, a mechanism of valley expansion is predicted to develop along all monitoring lines in the model.KeywordsCivil EngineeringDam impoundmentValley contraction/Expansion mechanismsNumerical studyFluid-mechanical analysis

Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox

Abstract: This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.

Deep Residual Shrinkage Networks for Fault Diagnosis

Abstract: This article develops new deep learning methods, namely, deep residual shrinkage networks, to improve the feature learning ability from highly noised vibration signals and achieve a high fault diagnosing accuracy. Soft thresholding is inserted as nonlinear transformation layers into the deep architectures to eliminate unimportant features. Moreover, considering that it is generally challenging to set proper values for the thresholds, the developed deep residual shrinkage networks integrate a few specialized neural networks as trainable modules to automatically determine the thresholds, so that professional expertise on signal processing is not required. The efficacy of the developed methods is validated through experiments with various types of noise.