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This paper applied the prevalent acoustic emission (AE) technology to identify the damage modes and critical conditions for FRP/steel-concrete composite beams during the failure process. AE signals generated by the structural damages were classified efficiently by using a novel self-adaptive real-time clustering (SARTC) method; damage modes corresponding to each clustering category were recognized and analyzed, and the dominant damage type at different stages was obtained by comparing the AE activities and feature values. By conducting the AE intensity analysis, the dynamic evolutionary mechanisms and critical conditions of composite beams were identified; the increase in intensity value from 0.2 to 0.3 reflects the process from critical yielding to major fracture. By establishing the non-linear fitting model between local response and cumulative AE energy, the instantaneous status at arbitrary local position of the composite beam can be inverted and predicted quantitatively by independent AE testing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tractor condition recognition has important research value in helping to understand the operating status of tractors and the trend of tillage depth changes in the field. Therefore, this article presents a method for recognizing tractor conditions, providing the basis for establishing the relationship between tractor conditions and the tillage depth of the attached agricultural machinery. This study designed a tractor condition recognition method based on neural networks. Using real-world vehicle data to establish a data set, K-means clustering analysis was used to label the data set based on four conditions: "accelerated start", "constant speed", "decelerated stop" and "turning". The learning vector quantization (LVQ) neural network and the VGG-16 model of a CNN were selected for use recognizing the tractor conditions. The results showed that both the neural networks had good recognition effects. The average accuracy rates of the VGG-16 model of CNN and LVQ neural network were 90.25% and 79.7%, respectively, indicating that these models could be applied to tractor condition recognition and provide theoretical support for the correction of angle detection errors. [ABSTRACT FROM AUTHOR]

This paper applied the prevalent acoustic emission (AE) technology to identify the damage modes and critical conditions for FRP/steel-concrete composite beams during the failure process. AE signals generated by the structural damages were classified efficiently by using a novel self-adaptive real-time clustering (SARTC) method; damage modes corresponding to each clustering category were recognized and analyzed, and the dominant damage type at different stages was obtained by comparing the AE activities and feature values. By conducting the AE intensity analysis, the dynamic evolutionary mechanisms and critical conditions of composite beams were identified; the increase in intensity value from 0.2 to 0.3 reflects the process from critical yielding to major fracture. By establishing the non-linear fitting model between local response and cumulative AE energy, the instantaneous status at arbitrary local position of the composite beam can be inverted and predicted quantitatively by independent AE testing.

When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Vibration, Measurement & Diagnosis is the property of Nanjing Hangkong Daxue and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When the gas pipeline leaks, it causes huge economic losses. This paper establishes a digital twin model of a pipeline based on the pressure signal generated by a pipeline leak and researches on pipeline leak detection. First, an online updating of the twin model is established to update the data of the physical information space and the parameters of the twin model online. Second, a visual model is established to display the spatial data of physical information of pipelines and output data of the digital twin of pipelines in real-time. If pipeline leakage is identified, an alarm would be triggered and a corresponding emergency rescue plan would be initiated based on the the leakage. Finally, the pipeline leakage identification model can be established by analysing the finite element model of the pipeline, and the sample data were obtained and preprocessed to extract the feature vectors. The training model of the Support vector machine (SVM) was used to classify the working conditions. Theoretical analysis and experimental results show that the method proposed in this paper has high detection accuracy, so it is feasible to judge gas pipeline leakage by using digital twin prediction.

The operating conditions influence the stability and consumption of a blast furnace. Recognizing these conditions makes changing the burden distribution parameters more efficient. The cooling stave temperature (CST) is a crucial state parameter that indicates the conditions of the process. Owing to the high data volume of the CST and the lack of methods for recognizing the stability of the slag crust, it is difficult for operators to recognize the conditions accurately according to the CST during the ironmaking process. Thus, in this study, a condition recognition method with information granulation for burden distribution in a blast furnace was presented. First, information granulation was employed to reduce the volume of the CST data and present it in a granular form. Then, considering the lack of a method for calculating the similarity of CST information granules, a novel fuzzy similarity calculation method was devised to calculate the membership grades of information granules belonging to different standard granules. Finally, the conditions were recognized according to the membership values. Experimental results based on industrial data demonstrated that the proposed method can be used to recognizes the conditions in the blast furnace. [ABSTRACT FROM AUTHOR]

Fuel-cell hybrid electric vehicles have the advantages of zero pollution and high efficiency and are extensively applied in commerce. An energy management strategy (EMS) directly impacts the fuel consumption and performance. Moreover, model prediction control (MPC) is synchronous and has been a research hotspot of EMS in recent years. The existing MPC's low-speed prediction accuracy, which results in considerable instability in EMS allocation, is solved by the proposed energy management strategy based on adaptive model prediction. Dynamic programming (DP) is used as the solver, improved condition recognition and a radial basis neural network (RBFNN) are used as the speed predictor, and hydrogen consumption and the state of charge (SOC) are used as the objective function. According to the simulation results, using a 5 s speed prediction improves the forecast accuracy by 9.75%, and compared with employing a rule-based energy management strategy, this strategy reduces hydrogen consumption and the power cell fluctuation frequency by 3.50%. [ABSTRACT FROM AUTHOR]

Tractor condition recognition has important research value in helping to understand the operating status of tractors and the trend of tillage depth changes in the field. Therefore, this article presents a method for recognizing tractor conditions, providing the basis for establishing the relationship between tractor conditions and the tillage depth of the attached agricultural machinery. This study designed a tractor condition recognition method based on neural networks. Using real-world vehicle data to establish a data set, K-means clustering analysis was used to label the data set based on four conditions: “accelerated start”, “constant speed”, “decelerated stop” and “turning”. The learning vector quantization (LVQ) neural network and the VGG-16 model of a CNN were selected for use recognizing the tractor conditions. The results showed that both the neural networks had good recognition effects. The average accuracy rates of the VGG-16 model of CNN and LVQ neural network were 90.25% and 79.7%, respectively, indicating that these models could be applied to tractor condition recognition and provide theoretical support for the correction of angle detection errors.

CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature perception and ultimately affect the learning effect and recognition accuracy. In order to solve this problem, the matching between the size of convolution kernel and the signal (rotation speed, sampling frequency) was optimized with the matching relation obtained. Through the study of this paper, the ability of extracting vibration features of CNN was improved, and the accuracy of vibration state recognition was finally improved to 98%. [ABSTRACT FROM AUTHOR]

Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms.

Authorized content is a type of content that can be generated only by a certain Content Provider (CP). The content copies delivered to a user may bring rewards to the CP if the content is adopted by the user. The overall reward obtained by the CP depends on the user’s degree of interest in the content and the user’s role in disseminating the content copies. Thus, to maximize the reward, the content provider is motivated to disseminate the authorized content to the most interested users. In this paper, we study how to effectively disseminate the authorized content in Interest-centric Opportunistic Social Networks (IOSNs) such that the reward is maximized. We first derive Social Connection Pattern (SCP) data to handle the challenging opportunistic connections in IOSNs and statistically analyze the interest distribution of the users contacted or connected. The SCP is used to predict the interests of possible contactors and connectors. Then, we propose our SCP-based Dissemination (SCPD) algorithm to calculate the optimum number of content copies to disseminate when two users meet. Our dataset based simulation shows that our SCPD algorithm is effective and efficient to disseminate the authorized content in IOSNs.

Proper burden distribution is an important way to improve conditions of a blast furnace, which indicate the stability and energy consumption. However, it is difficult for operators to recognize the stability and energy consumption at the same time and adjust them through burden distribution, due to complex iron-making mechanism. Therefore, this article presents a two-stage decision-making method for the burden distribution parameters based on recognizing the conditions. First, the stability and energy consumption are recognized, respectively, according to the classification of stability of slag crust (SSC) and the gas utilization rate (GUR). And then, two prediction models are presented for the SSC and GUR to predict the conditions. Finally, the burden distribution parameters are determined in two stages, where edge parameters of the burden distribution are determined at first according to the SSC, and then, center parameters are determined according to the GUR. Experimental results based on industrial data show that the proposed method provides an effective decision-making way for improving the conditions. [ABSTRACT FROM AUTHOR]

CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature perception and ultimately affect the learning effect and recognition accuracy. In order to solve this problem, the matching between the size of convolution kernel and the signal (rotation speed, sampling frequency) was optimized with the matching relation obtained. Through the study of this paper, the ability of extracting vibration features of CNN was improved, and the accuracy of vibration state recognition was finally improved to 98%.

A method based on Weibull distribution and GG fuzzy clustering is studied and proposed in order to solve the issue of healthy condition recognition for quayside container crane（QCC） reducer. Using envelope method to denoise data which caused by the complexity conditions firstly. Then the scale parameters and shape parameters are created using Weibull distribution fitting, which could show change characteristics of collected samples quantitatively. In order to recognize the reducer condition further, the GG fuzzy clustering method is used to divide the different stages of the healthy condition, realizing recognition for reducer healthy. Test data from NetCMAS system is adopted in living analysis, FCM and GK clustering algorithm are analyzed for contrast. The results show that the method proposed in this paper is effective and has better clustering effect.

Copyright of Journal of Petrochemical Universities / Shiyou Huagong Gaodeng Xuexiao Xuebao is the property of Journal Editorial Department Of Liaoning Shihua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cutting tools are direct performers in material cutting processes of NC machining, while the degree of cutter attrition and breakage directly influences the quality of production. Meanwhile tool condition recognition is a key technique in automatic and unmanned machining process. Aimed at this problem, the recognition of tool-condition based on machining feature in real-time is stated. Machining features are used to build relationships between geometry, processing technology and monitoring signals in order to provide the basis for cutting tool condition identification. A feature database is established with experimental results and a cutting tool condition recognition system is established based on this database. Experimental results suggested that the proposed method solves the problem of tool condition monitoring, especially for production with multi varieties and small-batches. [ABSTRACT FROM AUTHOR]

In order to recognize the different operating conditions of a distributed and complex electromechanical system in the process industry, this work proposed a novel method of condition recognition by combining complex network theory with phase space reconstruction. First, a condition-space with complete information was reconstructed based on phase space reconstruction, and each condition in the space was transformed into a node of a complex network. Second, the limited penetrable visibility graph method was applied to establish an undirected and un-weighted complex network for the reconstructed condition-space. Finally, the statistical properties of this network were calculated to recognize the different operating conditions. A case study of a real chemical plant was conducted to illustrate the analysis and application processes of the proposed method. The results showed that the method could effectively recognize the different conditions of electromechanical systems. A complex electromechanical system can be studied from the systematic and cyber perspectives, and the relationship between the network structure property and the system condition can also be analyzed by utilizing the proposed method. [ABSTRACT FROM AUTHOR]

The operation monitoring of multi-product pipelines helps to grasp the operation dynamics, detect abnormal situations in time, and assist on-site operation management. However, due to the complexity of the scheduling plan, the operating conditions of pipelines change frequently, which makes it difficult to accurately recognise condition types. To solve the above problem, an intelligent monitoring framework for operating conditions is proposed to simultaneously achieve the system recognition of steady, unsteady, and abnormal conditions. (i) The proposed monitoring framework extracts temporal and spatial characteristics of condition samples through four modules: Modules 1 and 2 form an unsupervised model for monitoring state changes and capturing temporal characteristics of condition samples; Module 3 is utilised to capture the spatial characteristics; the fusion layer based on Module 4 is applied to nonlinearly fit the spatiotemporal characteristics, and while monitoring the status changes of condition, it can also accurately recognise whether the condition is normal operation adjustment or abnormal condition. (ii) Taking a simulated pipeline and a real pipeline as examples, the effectiveness of the proposed monitoring framework is verified, and the accuracy, precision, recall, and F1 score of the recognition results reach 98.56%, 98.56%, 97.68%, and 98.12%. (iii) Through the sensitivity analysis of each module, accuracy, precision, recall, and F1 score are reduced to 96.10%, 96.10%, 95.83%, and 96.83% (i.e., only 2.46%, 2.46%, 1.85%, 1.29% differences) without Module I, which proves that the framework has strong robustness and generalisation. (iv) Finally, an intelligent analysis and control system of multi-product pipelines is designed for future applications. Consequently, the proposed intelligent monitoring framework can guide the safe operation and management of multi-product pipelines on-site. • An intelligent framework is proposed for operating conditions monitoring of multi-product pipelines. • An unsupervised method is proposed to monitor status changes. • A fusion layer is conducted to recognise whether operation adjustment or abnormal conditions. • Two pipelines are used to verify the effectiveness and reliability of the proposed framework. [ABSTRACT FROM AUTHOR]

• A method for constructing accelerated reliability test cycle of electric drive system based on vehicle operating data was presented. • Identification of typical driving conditions related to reliability was achieved by combining failure mechanisms and machine learning algorithms. • Cyclic numbers of driving sections in the load spectrum were determined by damage equivalent and multi-objective optimization method considering asynchronous failure of different components. • Loading sequence of accelerated test conditions was defined by the Markov principle correlating to actual user driving conditions. For the current traditional vehicle, reliability test specification is difficult to effectively cover the actual service load intensity of the electric drive system (EDS), the existing specification has the problems of insufficient evaluation of extreme operating conditions, the inaccurate definition of the whole life cycle damage target, and weak user relevance. Therefore, this paper proposes a method to construct an accelerated reliability test cycle for EDS based on 300 users' one-year operation data. By extracting the running fragments and constructing the characteristic parameters associated with the dominant failure load of the EDS. Principal component analysis and clustering algorithms identify the running fragments and obtain five typical operating conditions. Based on the unit damage intensity distribution model, the accelerated testing condition for simultaneous assessment of multiple components is selected, and each component's whole life cycle damage target is determined. To further consider the loading sequence of the fragments, a state transfer probability matrix for each condition is established based on the Markov chain model, which implements the condition combination. Using the multi-objective optimization algorithm determines the number of cycles for each condition and eventually compiles the accelerated reliability test cycle of the EDS. In addition, by conducting rig tests to verify the validity of the accelerated test load spectrum and determine the accelerated factor by the testing data. This study provides specific references and technical support for developing and validating the EDS. [ABSTRACT FROM AUTHOR]

• A costumed hybrid model is proposed for multi-product pipeline condition recognition based on process and data coupling. • A SPS model is established to simulate various unsteady and abnormal conditions. • Variable-length series processing is performed on the unsteady condition data. • A real pipeline and a simulated pipeline are used to verify the accuracy and stability of the proposed method. It is significant for field monitoring and management to identify the operating state of the pipeline and prevent an abnormal accident from occurring. Aiming at the few unsteady and abnormal conditions data samples, different condition switching duration, and strong time-space characteristics of multi-product pipeline condition recognition, a hybrid model for condition recognition based on process and data coupling is proposed. First, collecting the relevant parameters of the real multi-product pipeline system and using Stoner Pipeline Simulator (SPS) to establish a simulation model. Based on the simulation model, performing the simulation of unsteady conditions and abnormal conditions to obtain the corresponding simulated data. Second, the reliability of the simulation model is verified by calculating the distribution similarity between the simulated data and the real pipeline data. Third, the simulated data and real data are processed by variable-length series and input into the proposed hybrid model for training and verification. The results show that the proposed hybrid model has high accuracy, precision, recall, and F1 score of 93.1%, 93.1%, 87.2%, and 84.7%, respectively, which has essential guiding significance for monitoring and ensuring the safe and stable operation of multi-product pipelines. [ABSTRACT FROM AUTHOR]

In the coal-fire industry, recognition of burning condition is vital for combustion control and optimization as it can provide early warning of abnormal conditions in the combustion system. We propose an effective model based on spatiotemporal features extracted from flame video for burning condition recognition especially for unsteady state condition. This model extracts features from both the spatial and temporal dimensions based on multiple adjacent frames to capture the appearance and motion information of the flame. Two new three-dimensional (3D) descriptors are developed to characterize the dynamic characteristics of the flame video stream. A computationally simple and fast dynamic texture descriptor, 3DBLBP, is designed to extract flame dynamic texture and motion from three adjacent frames of a video block, and a dynamic structure descriptor, HOPC-TOP is developed by extracting 3D phase congruency information of video clip from three orthogonal planes to capture structure and motion characterizes of flame. The two descriptors are combined to extract spatiotemporal features from flame clip for burning condition recognition. Experiment results show the proposed framework can achieve a high recognition accuracy in the real-world data, thus verifying the effectiveness of our proposed framework for the recognition of burning conditions in a rotary kiln. • We develop an effective model based on spatiotemporal features of flame clip for the recognition of burning condition. • The unsteady state condition of rotary kiln is first studied in this paper, and the proposed method achieves high recognition accuracy for it. • We propose two computationally simple and effective 3D dynamic texture and structure descriptors, 3DBLBP and HOPC-TOP, to characterize dynamic texture and structure of flame. [ABSTRACT FROM AUTHOR]

• A PFC-CNN method is proposed for bearing vibration state recognition. • The PFC-CNN can also identify the fault type under different working loads. • T-SNE tool in the manifold learning method was used to visualize the results. • The bearing fault recognition accuracy of PFC-CNN is validated with CWRU data. To obtain comprehensive bearing vibration characteristic information and improved the accuracy of vibration state recognition, this paper proposed the pre-fully connected deep CNN (PFC-CNN) method and based on this, established the bearing vibration state recognition model. Firstly, the CNN's front fully connected structure was design and the front fully connected layer was used to reduce the complexity of the signal and extracted the global characteristics of the signal. On this basis, the fusion feature learning of global features and local features (which got by CNN's local perceptual field) was studied. Secondly, the back-propagation training of the whole network was studied, and the discrimination degree and recognition accuracy of the corresponding features of different state categories were finally improved. Results of experimental study showed that compared with other methods, the proposed method in this paper improved the identification accuracy which can reach 96.25%. [ABSTRACT FROM AUTHOR]

For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy. [ABSTRACT FROM AUTHOR]