A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis

Condition monitoring and fault diagnosis of diesel engines are of great significance for safety production and maintenance cost control. The digital twin method based on data-driven and physical model fusion has attracted more and more attention. However, the existing methods lack deeper integration and optimization facing complex physical systems. Most of the algorithms based on deep learning transform the data into the substitution of the physical model. The lack of interpretability of the deep learning diagnosis model limits its practical application. The attention mechanism is gradually developed to access interpretability. In this study, a digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis is proposed with considering its signal characteristics of strong angle domain correlation and transient non-stationary, in which a new soft threshold filter is designed to draw more attention to multi decentralized local fault information dynamically in real time. Based on this attention mechanism, the distribution of fault information in the original signal can be better visualized to help explain the fault mechanism. The valve failure experiment on a diesel engine test rig is conducted, of which the results show that the proposed adaptive sparse attention mechanism model has better training efficiency and clearer interpretability on the premise of maintaining performance.

Fault diagnosis of rolling bearings based on multi-scale deep subdomain adaptation network

This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.

Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network

The vibration signals of a planetary gearbox have the characteristics of strong background noise and instability and are non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the fault characteristic frequency amplitudes of bi-spectrum or bi-spectrum slices, or the further quantitative calculations of fault characteristic values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the entire domain information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum+CNN, local bi-spectrum+SVM, original vibration signal+CNN, and local bi-spectrum+BP neural networks showed that the method proposed in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

PurposeIntelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approachIn this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.FindingsThe Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.Originality/valueThe fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis

The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.

Intelligent Fault Diagnosis of Photoelectric Pod Bearing Based on Multi-information Fusion

Intelligent fault diagnosis technology has become the focus of research in various fields. Its realization depends on the acquisition of equipment state by sensors. Because the fault information provided by a single sensor has limitations and cannot fully reflect the fault state of the tested object, we need to use multiple sensors to collect and fuse the fault information of rolling bearings to ensure the accuracy and accuracy of intelligent fault diagnosis. Based on this, this paper analyzes the application of fuzzy rules of multi-sensor information fusion technology in the fault diagnosis of bearings in the optoelectronic pod, so as to provide a reference for the realization of intelligent fault diagnosis of each structure in the optoelectronic pod.

The Impact of 3D Finite-Fault Information on Ground-Motion Forecasting for Earthquake Early Warning

ABSTRACT We identify aspects of finite-source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite-fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D-source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line-source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D-source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground-motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.

Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.