State Identification Method Research Articles

Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature.

Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm

Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data

Throughout the huller shelling process, the rubber rollers progressively deteriorate. The velocity of the rubber rollers decreases as the distance between the rollers rises. These modifications significantly influence the rate at which rice hulling occurs. Hence, the implementation of real-time online detection is crucial for maintaining the operational efficiency of the huller. Currently, the prevailing inspection methods include manual inspection, 2D vision inspection, deep learning methods, and machine vision methods. Nevertheless, these conventional techniques lack the ability to provide detailed information about the faulty components, making it challenging to conduct comprehensive defect identification in three dimensions. To address this issue, point cloud technology has been incorporated into the overall detection of the working condition of the huller. Specifically, the Random Sample Consensus segmentation algorithm and the adaptive boundary extraction algorithm have been developed to identify abnormal wear on the rubber rollers by analyzing the point cloud data on their surface. A solution technique has been developed for the huller to compensate for the speed of the rubber rollers and calculate the mean values of their radii. Additionally, a numerical simulation algorithm is proposed to address the dynamic change in the roller spacing detection. The results show that point cloud data can be utilized to achieve real-time and precise correction of anomalous wear patterns on the surface of rubber rollers.

Operating state identification of hybrid power system based on back propagation neural network

Abstract This paper presents a system state identification method based on back propagation (BP) neural network. First, the working modes of the hybrid power system are given. Besides, the method of normalizing training parameters is provided to facilitate the training of neural network. Then, a neural network method is applied to state identification of hybrid power system. Finally, the weights and thresholds of the structure are obtained by the normalized training parameters, and the test results indicate that the trained neural network structure satisfies the system identification requirements.

Visible-hidden hybrid automatic feature engineering via multi-agent reinforcement learning

Feature engineering is one of the most important and time-consuming steps of machine learning algorithms. In recent years, automatic feature engineering (AFE) methods have received a lot of attention due to their low cost and scalability. However, the existing AFE methods do not take into account the interactions of features in the evolution process and cannot achieve effective feature selection, which weakens the performance of these methods. To tackle the above issues, a novel visible-hidden hybrid automatic feature engineering (VHAFE) method is proposed in this work. Specifically, a visible-hidden hybrid feature transformation graph (VHFTG) is devised to represent both various kinds of feature transformation functions and the feature selection process. Afterward, a multi-pointer state identification method is proposed, which enables the interactions among derived features and the efficient utilization of historically derived features. Furthermore, the multi-agent reinforcement learning algorithm is introduced to optimize the evolution process of VHFTG, where an input variable selection network is devised as the auxiliary policy network to avoid the generation of excessive noise features. Additionally, the VHAFE achieves the state-of-the-art results on a total of 19 public datasets and 1 dataset collected from the actual industrial operation process of gas turbines, which demonstrates the effectiveness of the proposed method.

Research on tool wear state identification method driven by multi-source information fusion and multi-dimension attention mechanism

Cutting tool wear condition monitoring technology is the key technology of advanced manufacturing system and a crucial component of machining. The stage of the tool wear has a direct impact on performance of the workpiece and efficacy of machine tool. However, the duplication and lack of wear information and the fuzzy area of the tool wear transition stage are the key factors contributing to the incorrect estimation of the tool wear state when extracting cutting tool wear feature information. As a consequence, this study presents a data fusion from several sources-based intelligent tool wear state detection technique. The fusion of data from several sources can effectively realize the complementarity of machining information. This provides the model with more comprehensive identification data. The mapping between wear state and wear characteristics is precisely established. To address these issues, the attention mechanism of channel and spatial latitude is integrated into the feature extraction. The model that was constructed in the present investigation has a comprehensive identification accuracy of 0.982. The F1 score of initial, normal and severe wear stage of tool wear are 0.977, 0.968 and 0.993, which are better than other models. Experiments show that the identification method proposed in this study may provide accurate tool wear condition identification based on machining process data, allowing for more flexible and precise cutting tool change decisions in machining.

A cylinder block dynamic characteristics-based data augmentation method for wear state identification under data imbalance condition

The deterioration of the wear state in the key friction pairs will degenerate the performance of the axial piston pump. The wear rates differ under different wear states in practical applications. Therefore, the amount of monitoring data is significantly different under different wear states, which causes data imbalance problem. In addition, the distribution characteristics of the external signals are often drowned by environmental noise. To address these issues, a novel cylinder block dynamic characteristics-based data augmentation method is presented for wear state identification. Firstly, a cylinder block’s dynamic characteristics state perception method is proposed to obtain the time domain data. Then, to reduce the computing costs, the raw data is fused into multi-channel time–frequency domain data by the short-time Fourier transform (STFT) based method. Finally, the minority time–frequency data is augmented by the designed deep convolutional generative adversarial network (DCGAN). To verify the wear state identification performance of the proposed method, a convolutional neural network (CNN) is designed. Wear states injection experiments are adopted to verify the feasibility of the proposed method. The advantage of the developed method is that it obtains prominent data distribution characteristics from internal signals to enhance the performance of the data augmentation process.

Research on structure state identification method of segmented pipeline based on distributed optical fiber sensing

Research on structure state identification method of segmented pipeline based on distributed optical fiber sensing

Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning

One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

Dynamic Project Expediting: A Stochastic Shortest-Path Approach

We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, are subject to precedence constraints, and progress randomly. We formulate a discrete-time, infinite-horizon Markov decision process in which the manager reviews progress in each period and decides which activities to expedite to balance expediting costs with delay costs. We derive structural properties for this dynamic project expediting problem. These enable us then to devise exact solution methods that we show to reduce computational burden significantly. We illustrate how our method generalizes and can be used to tackle a wide range of so-called stochastic shortest-path problems that are characterized by an intuitive property and can capture other applications, including medical decision-making and disease-modeling problems. Moreover, we also deal with the state identification issue for our problem, which is a challenging task in and of itself, owing to precedence constraints. We complement our analytical results with numerical experiments, demonstrating that both our solution and state identification methods significantly outperform extant methods for a supply chain example and for various randomly generated instances. This paper was accepted by Chung Piaw Teo, optimization. Funding: R. Mogre acknowledges support from the U.S.-UK Fulbright Commission and the Lloyd’s Tercentenary Research Foundation through the Fulbright-Lloyd’s Scholar Award, which allowed him to spend an extended period of time at Massachusetts Institute of Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4876 .

A Method for Rotor Speed Measurement and Operating State Identification of Hydro-Generator Units Based on YOLOv5

A Method for Rotor Speed Measurement and Operating State Identification of Hydro-Generator Units Based on YOLOv5

Identification of bolt state in lap joint based on propagation model and imaging methods of Lamb waves

Bolts are commonly applied to lap joints in various industrial fields. Harsh environment, low quality of operation, and other unfavorable conditions may result in bolt looseness. Due to the structural complexity, conventional Lamb wave-based methods just extract statistical characteristics for looseness detection, while neglecting the information contained in the propagation process of Lamb waves. To take full advantage of the information on bolt states carried by array signals during the propagation, this study proposes a detection procedure based on propagation model and imaging methods of Lamb waves. Firstly, the contact status on the macro and micro scales is investigated with theoretical analysis and finite element simulation, and the theoretical model of Lamb wave propagation across bolted joints is established deriving from the analysis of contact. Subsequently, three imaging methods, including delay-and-sum, minimum variance, and sparse reconstruction, are adapted for the identification of bolt state. Since Lamb waves are mainly transmitted into another plate through the contact area near the bolts based on the proposed propagation model, pixel values are inclined to be concentrated around the bolts. On this basis, imaging intensity near each individual bolt can reveal its fastening state given that the transmitted energy around its location is associated with its torque. Finally, experiments are carried out in both single-bolt and double-bolt joints. The proposed propagation model is verified in the single-bolt joint and loose bolts can be identified in the imaging results of the double-bolt joint, which demonstrates the effectiveness of the proposed method for bolt state identification.

An efficient analytical approach for operational reliability evaluation of integrated electricity‐heat energy systems with variable wind power

AbstractThe increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method.

State Identification of Power System Based on Visual Geometric Group Network

Accurate and timely identification of power system operation states are requisite for taking control strategies and ensuring power system security. With the high grid penetration of renewable energy generation and wide application of power electronic equipment, power system is becoming more complicated and facing more uncertainties. Traditional model based power system state identification methods are faced with challenges and data driven methods have great prospects. This paper proposes a new data-driven method for identifying power system operating states based on visual geometric group network VGG16 (16-layer Visual Geometry Group (VGG16). Firstly, the Lasso algorithm is used to select the operating state features of the power system to realize the features dimensionality reduction. Then, the weights of the loss functions are adjusted according to the difficulty of model for identifying power system states. Finally, the IEEE39 node system is used as an example, the effectiveness and robustness of the proposed method is verified.

Frame vibration states identification for corn harvester based on joint improved empirical mode decomposition - Support vector machine method.

The frame of corn harvester is prone to vibration bending and torsional deformation due to the vibration caused by field road bumps and fluctuations. It poses a serious challenge to the reliability of machinery. Therefore it is critical to explore the vibration mechanism, and to identify the vibration states under different working conditions. To address the above problem, a vibration state identification method is proposed in this paper. An improved empirical mode decomposition (EMD) algorithm was used to decrease noise for signals of high noise and non-stationary vibration in the field. The support vector machine (SVM) model was used for identification of frame vibration states under different working conditions. The results showed that: (1) an improved EMD algorithm could effectively reduce noise interference and restore the effective information of the original signal. (2) based on improved EMD - SVM method identify the vibration states of the frame with the accuracy of 99.21%. (3) The corn ears in grain tank were not sensitive to low order vibration, but had an absorption effect on high order vibration. The proposed method has the potential to be applied for accurately identifying vibration state and improving frame safety.

Data-driven methods for low-dimensional representation and state identification for the spatiotemporal structure of cavitation flow fields

Computational Fluid Dynamics (CFD) generates high-dimensional spatiotemporal data. The data-driven method approach to extracting physical information from CFD has attracted widespread concern in fluid mechanics. While good results have been obtained for some benchmark problems, the performance on complex flow field problems has not been extensively studied. In this paper, we use a dimensionality reduction approach to preserve the main features of the flow field. Based on this, we perform unsupervised identification of flow field states using a clustering approach that applies data-driven analysis to the spatiotemporal structure of complex three-dimensional unsteady cavitation flows. The result shows that the data-driven method can effectively represent the changes in the spatial structure of the unsteady flow field over time and to visualize changes in the quasi-periodic state of the flow. Furthermore, we demonstrate that the combination of principal component analysis and Toeplitz inverse covariance-based clustering can identify different states of the cavitated flow field with high accuracy. This suggests that the method has great potential for application in complex flow phenomena.

Research on mechanical state identification method of multi-type disconnectors based on difference curve and SVM

There are various types of high-voltage isolating switches and frequent mechanical defects, but it is difficult to meet engineering needs by detecting the mechanical state of a single isolating switch. Currently, most of the research only takes the GW4 disconnector as the research object, and the practicability of related diagnostic methods remains to be verified. In this paper, the difference curve is obtained as the sample to be identified by comparing the travel curve of the isolating switch to be diagnosed and its historical standard curve. With further elimination of the travel curve difference caused by the isolating switch model, extraction of the relevant time domain feature quantity, and combination of the support vector, the algorithm uses half of the data of each model in each state as the training sample and the other half of the data as the test sample, and the recognition accuracy rate is 100%. The GW4 and GW23 data are used as the training samples, the GW7 and GW36 data are used as the test samples, and the recognition accuracy is accurate. The rate was 87.5%. It is verified that the method of segmental extraction of difference curve features, combined with the support vector machine algorithm, can realize the intelligent identification of the mechanical state of different types of disconnectors, in which the degree of fault directly affects the spatial distribution of the samples and has a great impact on the accuracy of the identification results.

Channel State Identification in Complex Indoor Environments With ST-CNN and Transfer Learning

Accurate channel state identification can improve the accuracy of ultrawideband (UWB) indoor positioning. In complex indoor environments, existing channel state identification methods suffer from high costs. Meanwhile, these methods pay little attention to the different localization errors caused by weak -non-line-of-sight (WLOS) and non-line-of-sight (NLOS). In this letter, a channel state identification method that can identify line-of-sight (LOS), WLOS, NLOS is proposed for use in a single indoor scenario based on the Stockwell transform and convolutional neural networks (ST-CNN). Moreover, combined with transfer learning (TL), ST-CNN can reduce the cost of identification in complex indoor environments. Simulations based on open source datasets show that ST-CNN can achieves F2 scores of 97.17% and 97.14% in NLOS and WLOS. For complex indoor environments consisting of multiple indoor scenarios, compared with the ST-CNN, the ST-CNN+TL method can reduce the training time by 62% and the training data by 40%.

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Main circulation pump is the only high-speed rotating equipment in primary loop of nuclear power plant. Its function is to ensure the normal operation of primary loop system by controlling the circulating flow of reactor coolant. In order to ensure long-term healthy operation of nuclear power main circulating pump, a method for identifying the health states of nuclear power main circulating pump based on ensemble empirical mode decomposition (EEMD) and support vector machine optimized by optimized quantum genetic algorithm (OQGA-SVM) is proposed. Vibration signal of main circulating pump is decomposed by EEMD. Vibration signal characteristics of nuclear power main circulating pump in healthy state and different fault states are analyzed and target characteristic indexes are put forward. Then, health state identification model of main circulation pump of OQGA-SVM is established, and target characteristic indexes are used as input parameter of the model. Finally, combined with experimental data, the model analysis and validation show that the health state identification method of nuclear power main circulating pump based on EEMD-OQGA-SVM can accurately and effectively identify the states of main circulation pump, has a higher identification accuracy than EEMD-SVM method and is more efficient and accurate than EEMD-QGA-SVM method.