Query: minghang zhao

title: Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes
abstract: One of the significant tasks in data driven fault diagnosis methods is to configure a good feature set involving statistical parameters. However, statistical parameters are often incapable of representing the dynamic behavior of planetary gearboxes under variable operating conditions. Although the use of deep learning algorithms to find a good set of features for fault diagnosis has somewhat improved diagnostic performance, the lack of domain knowledge incorporated into deep learning algorithms has limited further improvement. Accordingly, this paper developed a variant of deep residual networks (DRNs) the so called deep residual networks with dynamically weighted wavelet coefficients (DRN+DWWC) to improve diagnostic performance, which takes a series of sets of wavelet packet coefficients on various frequency bands as an input. Further, the fact that no general consensus has been reached as to which frequency band contains the most intrinsic information about a planetary gearbox's health status calls for "dynamic weighting layers" in the DRN+DWWC and the role of the layers is to dynamically adjust a weight applied to each set of wavelet packet coefficients to find a discriminative set of features that will be further used for planetary gearbox fault diagnosis.

title: Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis
abstract: Wavelet transform, an effective tool to decompose signals into a series of frequency bands, has been widely used for vibration based fault diagnosis in machinery. Likewise, the use of deep learning algorithms is becoming popular to automatically learn discriminative features from input data for the sake of improving diagnostic performance. However, the fact that no general consensus has been reached as to which wavelet basis functions are useful for diagnosis motivated this investigation of methods to fuse multiple wavelet transforms into deep learning algorithms. In this paper, two methods i.e. multiple wavelet coefficients fusion in deep residual networks by concatenation and multiple wavelet coefficients fusion in deep residual networks by maximization were developed to capture discriminative information from diverse sets of wavelet coefficients for fault diagnosis. The efficacy of the developed methods was verified by applying them to planetary gearbox fault diagnosis.

title: Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis
abstract: Vibration signals under the same health state often have large differences due to changes in operating conditions. Likewise, the differences among vibration signals under different health states can be small under some operating conditions. Traditional deep learning methods apply fixed nonlinear transformations to all the input signals, which have a negative impact on the discriminative feature learning ability, i.e. projecting the intraclass signals into the same region and the interclass signals into distant regions. Aiming at this issue, this article develops a new activation function, i.e. adaptively parametric rectifier linear units, and inserts the activation function into deep residual networks to improve the feature learning ability, so that each input signal is trained to have its own set of nonlinear transformations. To be specific, a subnetwork is inserted as an embedded module to learn slopes to be used in the nonlinear transformation. The slopes are dependent on the input signal, and thereby the developed method has more flexible nonlinear transformations than the traditional deep learning methods. Finally, the improved performance of the developed method in learning discriminative features has been validated through fault diagnosis applications.

title: Multiple wavelet regularized deep residual networks for fault diagnosis
abstract: As an emerging deep learning method, deep residual networks are gradually becoming popular in the research field of machine fault diagnosis. A significant task in deep residual network based fault diagnosis is to prevent overfitting, which is often a major reason for low diagnostic accuracy when there is insufficient training data. This paper develops a multiple wavelet regularized deep residual network (MWR DRN) model that uses one wavelet basis function (WBF) as the primary WBF and other WBFs as the auxiliary WBFs. "Regularized" means that a constraint or restriction is applied to yield a high performance on the testing data. To be specific, the developed MWR DRN model is trained not only by the 2D matrices from the primary WBF, but also by the 2D matrices from the auxiliary WBFs using a stochastic selection strategy. Experimental results validate the effectiveness of the developed MWR DRN in improving diagnostic accuracy.

title: Multi block domain adaptation with central moment discrepancy for fault diagnosis
abstract: Despite the recent advances of deep neural network based domain adaptation for fault diagnosis under different working conditions, the degradation of feature transferability in top layers and incomplete elimination of mismatch between different working conditions hinder its further development. Accordingly, this study develops a new deep transfer method, i.e. multi block domain adaptation, which embeds the central moment discrepancy adaptation at multiple dense blocks to work collaboratively with densely connected convolutional networks to improve the transferability of learned features. First, a densely connected convolutional network is constructed as the main architecture. Second, the central moment discrepancy is adopted for minimizing the domains discrepancy at each dense block inside the network. Finally, the training datasets are fed into the model for training, then the learned model is used to perform the fault diagnosis. The experiment on the planetary gearbox is conducted to validate the effectiveness of the developed method.

title: Regression model for civil aero engine gas path parameter deviation based on deep domain adaptation with Res BP neural network
abstract: The variations in gas path parameter deviations can fully reflect the healthy state of aero engine gas path components and units; therefore, airlines usually take them as key parameters for monitoring the aero engine gas path performance state and conducting fault diagnosis. In the past, the airlines could not obtain deviations autonomously. At present, a data driven method based on an aero engine dataset with a large sample size can be utilized to obtain the deviations. However, it is still difficult to utilize aero engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks. To obtain monitoring autonomy of each aero engine model, it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero engine models. This paper adopts the Residual Back Propagation Neural Network (Res BPNN) to deeply extract high level features and stacks multi layer Multi Kernel Maximum Mean Discrepancy (MK MMD) adaptation layers to map the extracted high level features to Reproduce the Kernel Hilbert Space (RKHS) for discrepancy measurement. To further reduce the distribution discrepancy of each aero engine model, the method of maximizing domain confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible, and then the learned features can be confused. Through the above methods, domain invariant features can be extracted, and the optimal adaptation effect can be achieved. Finally, the effectiveness of the proposed method is verified by using cruise data from different civil aero engine models and compared with other transfer learning algorithms.

title: A novel anomaly detection method for gas turbines using weight agnostic neural network search
abstract: Anomaly detection of aircraft gas turbines is a small sample size classification problem. Under the condition of small observation samples, a significant challenge is to determine the scale of the neural network. The inappropriate scale might cause test accuracy to be low due to either overfitting or underfitting problems. In this paper, a novel anomaly detection method based on weight agnostic neural network search is investigated for gas turbines. On one hand, to prevent underfitting, the scale of the network evolves from small to large using inserting node, adding connection, or changing activation until it can reflect the complex relationship between the gas turbine health state and the gas path performance parameters. On the other hand, the number of connections in the network is considered as a term in the objective function, which can avoid overfitting in network evolution. Moreover, this paper proposes a new threshold selection method based on time series tendency turning point, which is effective in distinguishing normal patterns from abnormal patterns under the circumstance of unbalanced data. The effectiveness of the proposed model is verified using real life monitoring data from CFM56 7B gas turbines.

title: Bearing remaining useful life estimation based on time frequency representation and supervised dimensionality reduction
abstract: The extraction of ideal age feature is a challenging task in vibration based bearing remaining useful life (RUL) estimation. Aiming at this problem, a new approach is proposed on the basis of time frequency representation (TFR) and supervised dimensionality reduction. Firstly, S transform and Gaussian pyramid are employed to obtain TFRs at multiple scales. Textural features of TFRs are used as the high dimensional features. Then, a two step supervised dimensionality reduction technique, i.e. principal component analysis (PCA) plus linear discriminant analysis, is employed to reduce the dimensionality, in which the target dimension and number of classes are taken as variable parameters. Finally, the simple multiple linear regression model is utilized to estimate the RUL. Experimental results indicate that the proposed approach outperforms the methods using traditional statistical features and/or PCA. Additionally, variable conditions of load and speed should be considered in the future to further improve the proposed approach.

title: Regression Model for Civil Aero engine Gas Path Parameter Deviations Based on Res BP Neural Network
abstract: The gas path parameter deviations as crucial parameters can assist each airline to realize the performance state trend analysis, life prediction and fault diagnosis of aero engine. However, the calculation of gas path parameter deviations is complicated and the calculation models are also mastered by the original equipment manufacturer (OEM) which makes it burdensome for airlines to independently analyze the gas path performance of the aeroengine. At present, airlines have accumulated a large number of samples of gas path parameter deviations, which makes it possible to establish a regression model between gas path parameters and its deviations by data driven method. In order to enhance the analysis capability of airline in gas path performance, we apply the residual learning blocks to the back propagation (BP) neural network based on the learning mechanism of the residual networks (ResNets) According to the solution characteristics of gas path parameter deviations, the regression models for the gas path parameter deviations are established based on Res BP neural network. The screening for nonlinear independent variables of regression model is carried out by mean impact value (MIV) method, and then the input and output of Res BP neural network can be determined. After the regression model training, the test set is tested by the proposed regression model. By comparing with BP neural network regression model and traditional regression model, the proposed regression model manifests higher prediction accuracy and generalization performance on the three key gas path parameter deviations, which is of great guiding significance for the aero engine condition monitoring.

title: Fault diagnosis of rolling element bearing based on S transform and gray level co occurrence matrix
abstract: Time frequency analysis is an effective tool to extract machinery health information contained in non stationary vibration signals. Various time frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time frequency representations (TFRs) although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co occurrence matrix (GLCM) and multi class support vector machine (Multi SVM) Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi SVM is used for the classification. Experimental results indicate that the GLCM based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time domain and frequency domain features, wavelet packet node energy or two direction 2D linear discriminant analysis based features of the same TFRs in most cases.