Multi-scale spatio-temporal data modelling and brain-like intelligent optimisation strategies in power equipment operation and inspection

A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis

In gas turbine rotor systems, an intelligent data-driven fault diagnosis method is an important means to monitor the health status of the gas turbine, and it is necessary to obtain sufficient fault data to train the intelligent diagnosis model. In the actual operation of a gas turbine, the collected gas turbine fault data are limited, and the small and imbalanced fault samples seriously affect the accuracy of the fault diagnosis method. Focusing on the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signals is proposed here, and a structural optimization of the generator and a gradient penalty improvement in the loss function are introduced to generate effective fault data and improve the classification accuracy. The experimental results of the gas turbine test bench demonstrate that the proposed method can generate effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improve the fault classification and diagnosis performance of gas turbine rotors in the case of small samples, and provide an effective method for gas turbine fault diagnosis.

Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection

Artificial intelligence technology is widely used in mechanical system fault diagnosis as an effective means, but there are few fault samples in practice, which seriously restricts the industrial application of Al diagnosis model to high-precision diagnosis. In order to overcome the lack of fault samples, a fault diagnosis method composed of Gaussian cloud model and domain-invariant features extraction is proposed for expanding fault training samples in this paper. The method include three steps. Firstly, the limited measured samples is obtained by calculating the time-domain feature indexes of original signals. Secondly, the Gauss cloud model is construct based on the measured samples, and perform the forward cloud computing to generate a large amounts of cloud drops as simulated samples. Finally, based on the cross-domain transfer learning framework, the convolutional neural network (CNN) is utilized to extract a large number of domain-invariant features from the measured and simulated samples as the expanded fault samples to train the intelligent fault diagnosis model. The experimental investigation is carried out based on the experimental gear data respectively, and the experimental results show that the proposed method can effectively expand the fault samples to train the fault diagnosis model.

The paper aims to address the issue of insufficient fault samples in UHV converter equipment, which hinders their intelligent operation and inspection. For the operation and inspection of UHV converter equipment, this paper suggests a multimodal brain-like learning sample spatio-temporal correlation generation method. This method grabs typical fault samples from the defect-fault development time sequence process and creates samples using the nearest-neighbor generating segment technique by fusing the time sequence evolution law and the similarity of the adjacent samples. Based on the physical model of the converter and converter valve, we analyze the fault development laws of partial discharge, high temperature overheating, and micro-motion wear. The multimodal fault sample generation model with an embedded fault mechanism is established by integrating the time-sequence fault evolution mechanism and the spatial correlation between multimodal state quantities. The simulation demonstrates that brain-like learning generates samples embedded in the fault evolution laws of converter partial discharge and converter valve IGBT micromotion wear in 539 columns, encompassing 376 converter and 163 converter valve cases. The consistency between the generated samples and the actual samples exceeds 90%, thereby facilitating the training of brain-like models for health assessment of extra-high-voltage converter equipment, fault diagnosis, and trend prediction.

Physical model driven fault diagnosis method for shield Machine hydraulic system

In the field of fault diagnosis, composite fault are difficult to diagnose accurately because of the coupling effect between various faults and dynamic working conditions (load, rotating speed, etc.). In addition, fault samples under specific operating conditions of practical manufacturing processes suffer from inadequacy and unbalanced distribution. Aiming at the problem of sample imbalance in the process of model building, this paper takes the bearing and gear of gearbox as the research object and presents a fault diagnosis method of the gearbox with less sample information. A Deep Convolutional Generative Adversarial Network model (DCGAN) is constructed for a more accurate fault diagnosis, where the counter-learning mechanism is used to train the generator to learn the distribution characteristic of the original fault samples and to generate additional fault samples. Fault samples under various working conditions were collected through the designed experimental platform for the training of the fault diagnosis model. The experiments show that the DCGAN fault diagnosis model can effectively improve fault diagnosis accuracy with insufficient diagnosis samples.

This paper develops a new adaptive interpretable fault detection and diagnosis model (AIFD ) based on belief rule base (BRB) for complex system. The developed AIFD model aims to solve three problems in engineering practice: few fault samples, complex system mechanism and loss of interpretability in modeling process. The first two problems can be addressed by fusing expert knowledge and observation data in the BRB expert system. Moreover, to address the problem of loss of interpretability of AIFD model in its modeling process, a new belief rule weight adjustment method is proposed based on its sensitivity coefficient. The new adjustment method can improve the confidence degree of the users to the FDD output. The developed adjustment method can guarantee the fault diagnosis accuracy and the model interpretability. Then, to further address the influence of uncertain expert knowledge, a new optimization model is developed for the BRB based fault diagnosis model. To illustrate the effectiveness of the developed model, a case study of electric servo mechanism is conducted in this paper.

Predictive maintenance integrates equipment condition monitoring, fault diagnosis, fault prediction, maintenance decision support and maintenance activities. Intelligent manufacturing upgrades need to match the synchronous improvement of predictive maintenance capabilities, and predictive maintenance is the basic guarantee for enterprises to achieve intelligent manufacturing. Take the equipment condition monitoring and diagnosis system applicate in process industry as an example, this paper proposes IOT and operation big data analysis to equipment fault monitoring, diagnosis and preventive maintenance. Under the three-layer system framework of perception layer, network layer and application layer, machine learning algorithm is applied to carry out data mining on the equipment operation big data, establish expert knowledge base, obtain diagnosis rules, and realize the intelligent and efficient management mode integrating online monitoring, remote monitoring, remote diagnosis, fault matching and identification. Based on IOT and operation large data analysis, equipment health intelligent diagnosis provides the basic guarantee for equipment intelligent operation and maintenance, which helping enterprise establish new equipment management, maintenance, inspection and repair system under the concept of predictive maintenance and proactive maintenance.

SummaryThe issue of fault diagnosis in power equipment is receiving increasing attention from scholars. Due to the important role played by bearings in power equipment, bearing faults have become the main factor causing the shutdown of wind turbines units. Therefore, this paper takes bearing equipment as an example for research. In order to solve the problem of insufficient and unbalanced fault sample data of wind turbines bearings, a fault diagnosis (FD) method based on variational autoencoder and semi‐supervised learning is proposed in this paper. Firstly, based on Label Propagation‐random forests (LP‐RFs) and a small number of labeled fault samples, a semi‐supervised learning algorithm is proposed to label the original data samples. Secondly, a small number of training samples are preprocessed by the variational autoencoder to reduce the imbalance of the fault samples. Then, the RFs‐based method is adopted to train the processed fault samples to obtain a mature FD classifier. Finally, the proposed method is applied to FD for bearings, and the results show that the proposed method can realize bearings fault diagnosis (BFD). And meanwhile, the proposed method can also be applied for fault diagnosis in power transmission and transformation systems.

With the rapid development of converters in a variety of industrial fields, the fault diagnosis of power switching devices has become an important factor in ensuring the safe and reliable operation of related systems. In recent years, machine learning has performed well in many fault diagnosis tasks. The success of these advanced methods depends on sufficient marked samples for each fault type. However, in most industrial applications, it is expensive and difficult to collect fault samples, and the fault diagnosis model trained under the limited samples cannot meet the requirements of fault diagnosis accuracy. In order to solve this problem, this study proposes a few-shot learning method based on fault sample generation to realize the open-circuit fault diagnosis of IGBT in a three-phase PWM converter. This method is the deformation of the auto-encoder called the disturbance auto-encoder generation model. By designing the model structure and training algorithm constraints, the encoder learns the nonlinear transferable disturbance from the normal operating sample pairs. Then, the disturbance is applied to the decoder to synthesize new fault samples to realize the training of the fault diagnosis model with limited samples. The biggest advantage of this method is that it can achieve 95.90% fault diagnosis accuracy by only collecting the samples in the normal operating conditions of the target system. Finally, the feasibility and advantages of the proposed method are verified by comparative experiments.

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery

Despite the complicated fault mechanism of power equipment, with the increasing promotion and development of the Ubiquitous Power Internet of Things (UPIoT), the fault information of power equipment can be instantly saved, which makes possible intelligent diagnosis via fault samples. This study proposes a new method to implement comprehensive intelligent diagnosis by adopting the ShuffleNet lightweight convolution neural network (SLCNN). Considering the requirements of the UPIoT intelligent terminal, this study constructs six models, which are measured in terms of recognition accuracy, model storage, and calculation cost when applied to insulation and mechanical datasets. Compared with the existing models, the SLCNN outperforms them significantly in terms of recognition accuracy, with an accuracy of 95.77% and 99.9% in insulation and mechanical fault diagnoses, respectively. The SLCNN also demonstrates obvious advantages in other performance indicators, all of which contribute to its use in the accurate and reliable fault diagnosis of power equipment in the UPIoT context. Furthermore, through comparing feature maps, it is discovered that the aliasing degree and boundary of insulation defects are not as obvious as those of mechanical faults, which means that insulation fault diagnosis is much more difficult than mechanical fault diagnosis.

Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.