Robustness Of Fault Diagnosis Research Articles

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network (GGRU), one-dimensional convolutional neural network (1DCNN), and adaptive batch normalization (AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network’s feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model’s domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University, aero-engine bearings, Xi’an Jiaotong University, and the Changxing Sumyoung Technology. The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

Intelligent Fault Diagnosis of Variable-Condition Motors Using a Dual-Mode Fusion Attention Residual

Electric motors play a crucial role in ship systems. Detecting potential issues with electric motors is a critical aspect of ship fault diagnosis. Fault diagnosis in motors is often challenging due to limited and noisy vibration signals. Existing deep learning methods struggle to extract the underlying correlation between samples while being susceptible to noise interference during the feature extraction process. To overcome these issues, this study proposes an intelligent bimodal fusion attention residual model. Firstly, the vibration signal to be encoded undergoes demodulation and is divided into high and low frequencies using the IEEMD (Improved Ensemble Empirical Mode Decomposition) composed of the EEMD (Ensemble Empirical Mode Decomposition) and the MASM (the Mean of the Standardized Accumulated Modes). Subsequently, the high-frequency component is effectively denoised using the wavelet packet threshold method. Secondly, current data and vibration signals are transformed into two-dimensional images using the Gramian Angular Summation Field (GASF) and aggregated into a bimodal Gramian Angle Field diagram. Finally, the proposed model incorporates the Self-Attention Squeeze-and-Excitation Networks (SE) mechanism with the Swish activation function and utilizes the ResNeXt architecture with a Dropout layer to identify and diagnose faults in the multi-mode fusion dataset of motors under various working conditions. Based on the experimental results, a comprehensive discussion and analysis were conducted to evaluate the performance of the proposed intelligent bimodal fusion attention residual model. The results demonstrated that, in comparison to traditional methods and other deep learning models, the proposed model effectively utilized multimodal data, thereby enhancing the accuracy and robustness of fault diagnosis. The introduction of attention mechanisms and residual learning enable the model to focus more effectively on crucial modal data and learn the correlations between modalities, thus improving the overall performance of fault diagnosis.

XGBoost-Based Intelligent Decision Making of HVDC System with Knowledge Graph

This study aims to achieve intelligent decision making in HVDC systems in the framework of knowledge graphs (KGs). First, the whole life cycle KG of an HVDC system was established by combining intelligent decision making. Then, fault diagnosis was studied as a typical case study, and an intelligent decision-making method for HVDC systems based on XGBoost that significantly improved the speed, accuracy, and robustness of fault diagnosis was designed. It is noteworthy that the dataset used in this study was extracted in the framework of KGs, and the intelligent decision making of KG and HVDC systems was accordingly combined. Four kinds of fault data extracted from KGs were firstly preprocessed, and their features were simultaneously trained. Then, sensitive weights were set, and the pre-computed sample weights were put into the XGBoost model for training. Finally, the trained test set was substituted into the XGBoost classification model after training to obtain the classification results, and the recognition accuracy was calculated by means of a comparison with the standard labels. To further verify the effectiveness of the proposed method, back propagation (BP) neural network, probabilistic neural network (PNN), and classification tree were adopted for validation on the same fault dataset. The experimental results show that the XGBoost used in this paper could achieve accuracy of over 87% in multiple groups of tests, with recognition accuracy and robustness being higher than those of its competitors. Therefore, the method proposed in this paper can effectively identify and diagnose faults in HVDC systems under different operation conditions.

Fast nonlinear blind deconvolution for rotating machinery fault diagnosis

Sparse optimization based early fault diagnosis method is drawing more and more attention. In these methods, the objective function is usually a sparsity measure which can represent the impulsive signature produced by the mechanical impacts between rotating components. Therefore, the key point is the fault representation and convergence stability of the objective function. Inspired by this, fast nonlinear blind deconvolution algorithm is proposed for early fault diagnosis of rotating machinery. First, sigmoid function is developed to the generalized form to improve the fault representation ability of the objective function under noisy environment. The nonlinear mapping and sparse expression ability are discussed in detail. Then, Gaussian fitting window function and L1/2 penalty of filter are used to improve the distribution and performance of the weight vector. Finally, simulation data and early bearing fault data are employed to verify the effectiveness and determine optimal parameter setting of the proposed method. Based on the comparison results with the existing methods, the proposed method is found to be a promising method for the early-stage fault diagnosis, which can significantly improve the noise adaptability, computation effectiveness and robustness of fault diagnosis.

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.

Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification

Fault feature selection and early fault diagnosis for planetary gearbox are important tasks and have been widely investigate. This paper proposes a novel fault diagnosis scheme for planetary gearbox using multi-criteria fault feature selection and heterogeneous ensemble learning classification. Vibration signals collected by the acceleration sensors are imported for fault diagnosis of planetary gearbox. High dimension fault features are extracted by analyzing the vibration signals in time domain, frequency domain and time-frequency domain. The criteria for selecting lower dimension optimal fault features of planetary gearbox are designed, and the mathematic model for fault feature selection with multi-criteria is established. After that, a new feature selection method using Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is applied to obtain diverse lower dimension quasi optimal fault feature subsets. Then each quasi optimal fault feature subset is transferred to a base classifier for primary fault diagnosis. Those base classifications are performed by support vector machine and sparse Bayesian extreme learning machine respectively. Dezert-Smarandache rules are applied for classifier-level fusion to achieve and evaluate overall accuracy of the fault diagnosis for planetary gearbox. Experimental results state that the proposed method constantly gets diverse lower dimension quasi optimal fault features smoothly, and significantly improves the accuracy and robustness of fault diagnosis.

Fault Diagnosis of Nonlinear Uncertain Systems with Triangular Form

A novel approach to fault diagnosis for a class of nonlinear uncertain systems with triangular form is proposed in this paper. It is based on the extended state observer (ESO) of the active disturbance rejection controller and linearization of dynamic compensation. Firstly, an ESO is designed to jointly estimate the states and the combination of uncertainty, faults, and nonlinear function of nonlinear uncertain systems. It can derive the estimation of nonlinear function via the state estimations and system model. Then, linearization of dynamic compensation is employed to linearize the system by offsetting nonlinear function mandatorily using its estimation. An observer-based residual generator is designed on the basis of the prior linearized model for fault diagnosis. Moreover, threshold treatment technique is adopted to improve the robustness of fault diagnosis. This method is utilizable and simple in construction and parameter tuning. And also we show the construction of ESO and give the corresponding convergence proof succinctly. Finally, a numerical example is presented to illustrate the validity of the proposed fault diagnosis scheme.

Single fault isolation of a Hydrostatic Transmission System: Simulation and Experimentation

In this article, different methodologies in the area of system identification, parametric fault isolation, robustness in fault diagnosis have been surveyed and applied to a pedagogical HydroStatic Transmission (HST) system. A Bond Graph (BG) model for the system has been developed and then nonlinear regression analysis was done on the state equations, obtained from that model, to estimate the unknown parameters as a part of system identification. The test bench was designed to introduce faults in different components for the purpose of experimental validation of Fault Detection and Isolation (FDI). The model based single fault isolation was done by using structural analysis of some constraint relations, called Analytical Redundancy Relations (ARR), the numerical evaluation of which are called residuals. The robustness in fault isolation was addressed through Linear Fractional Transformation (LFT) approach to make residuals insensitive to uncertainty of system parameters, but sensitive to fault.

LPV Model-Based Fault Diagnosis Using Relative Fault Sensitivity Signature Approach in a PEM Fuel Cell.

In this paper, the problem of fault diagnosis in a PEM Fuel Cell (PEM FC) system using a model-based approach is addressed. A Linear Parameter Varying (LPV) model is used to catch the non linear dynamics of the PEM FC. A new fault diagnosis method is proposed based on structured residuals generation and sensitivity analysis evaluation. Robustness in fault diagnosis is increased by use of a relative fault sensitivity approach. The proposed methodology is applied to a Polymer Electrolyt Membrane Fuel Cell system. Finally, simulation results using the proposed approach presents a better isolability performance than classical Boolean fault signature approach.