Failure Identification Research Articles

Damage and deformation behavior of reinforced concrete pipes with varying joints under surface explosion

Reinforced concrete pipes (RCP) are the mainstay of urban water transmission networks. Urban development entails increased potential for blasting activities (such as subway tunneling, excavation, and demolition) as well as the risk of accidental explosions. This paper provides a detailed description of the damage evolution and deformation process of RCP under explosive loads using validated numerical simulation methods. A concise phenomenon-based method is introduced for RCP damage grading. Furthermore, a comparative analysis is conducted on the damage and deformation of RCP resulting from explosions at different locations and with varying weights. At last, this paper especially investigates and explains the impact of four commonly used joint types on the RCP’s response to explosions. The research results help to enhance the understanding of damage evolution and deformation behavior of RCP (or similar structures) induced by explosion, and are also references for protection, repairing and failure identification of segmented structures subjected to explosion.

Dynamic time scales ensemble framework for similarity-based remaining useful life prediction under multiple failure modes

In modern industry, the stochastic degradation of mechanical equipment typically involves multiple failure modes, which heavily affects the reliability of the remaining useful life (RUL) prediction. The similarity-based methods have been widely deployed in RUL prediction due to their flexibility, but it is still challenging to accurately identify similar degradation trajectories under varying failure modes. The obstacles lie in the interference of reference trajectories under different degradation states and the insufficiency of measuring trajectory trends. Therefore, this paper proposes a dynamic scales ensemble method based on the mean removal Canberra distance with failure identification (FI-MRC-DSE) for similarity-based prognosis. Firstly, a gated recurrent unit autoencoder network is employed to adaptively extract failure features from multi-dimensional monitoring data to support the targeted selection of reference trajectories. Then, the similarity matching is performed based on the proposed MRC distance instead of the commonly used Euclidean distance, enhancing the perception of degradation trends. Finally, the matching results across multiple time scales, which are dynamically determined by the instance's degradation state, are integrated to obtain the predicted RUL. It effectively overcomes the insufficient utilization of trajectory caused by the single time scale. In the experiments, the superiority of our developed similarity-based FI-MRC-DSE method is demonstrated by comparison with the state-of-the-art similarity-based methods. The effectiveness analyses and the ablation study show that all three key components contribute to accurate prognosis under multiple failure modes.

Numerical modelling of 3D concrete printing: material models, boundary conditions and failure identification

3D concrete printing (3DCP) attracts significant attention as an innovative manufacturing technology for the construction industry. As one of the challenges in 3DCP, failure mechanisms of 3D printed concrete structures were not well understood yet and hard to predict. The three-dimensional finite element (FE) method is an effective method to simulate such a layer-by-layer process. However, some existing technical issues in FE modelling, including additional initial deformations, failure identification, selection of material models, concrete foundation interactions and initial imperfections, need to be addressed for accurate simulation of 3DCP. In this study, FE models using a novel tracing element approach are developed to capture mechanical behaviours and failure modes of typical 3D printed concrete structures. The developed FE models was validated by comparing the obtained numerical results with those data available in literature. Furthermore, four material constitutive models are investigated analytically and numerically to compare their applicability in modelling 3D printed concrete structures. The obtained results show that the Mohr-Coulomb and Concrete Damage Plasticity (CDP) models can accurately predict failure behaviours of 3D printed concrete structures.

Extended cyclic innovation model as a tool for failure identification in innovation management. Case study of 25 years of CMOS image sensor technology in Belgium

This paper focuses on 25 years of CMOS image sensors in Belgium, starting with a successful university spin-off in the late 1990s. We rely on exploratory research using semi-structured interviews with this spin-off’s key protagonists. We follow them through time: founding five small businesses and being subject to three take-overs by multinationals. Using the Cyclic Innovation Model as a reference frame, we enlarge upon its Entrepreneurship Node and add Organizational Context to the model. Applying the model on the businesses we reviewed, we also demonstrate its usefulness in identifying failures in a company’s innovation management.

Failure identification and location in asphalt materials using coplanar capacitance technology

Compared to traditional non-destructive testing (NDT) methods, coplanar capacitance technology has advantages in intuitive identification and accurate location for identifying and locating the various failures in asphalt materials. At present, research on coplanar capacitance technology for various failure detection and location in asphalt materials is missing. Therefore, this paper aims to develop a various failure detection and location method in asphalt materials using coplanar capacitance technology. Firstly, the capacitance distribution measurement on the single-pair electrode capacitance sensor with constant area (CSCS) is utilized to locate the various failures of asphalt materials. Then, the various failure depth inside asphalt materials can be predicted using a standard curve of the various failure depth-maximum change capacitance. Finally, based on the single-pair electrode capacitance sensor with variable area (VSCS), the spatially resolved capacitance measurement determines the various failure shape details of asphalt materials. It is found that the Ultrasonic Tomography Technology (UTT) method can only identify internal various failures in asphalt materials and is unable to determine the location of various failures. Nevertheless, the capacitance distribution measurement can identify air void and water intrusion various failures in asphalt materials. And the relative error of the measured location results inside asphalt materials is within 7. The predicted curves of various failure depths in asphalt materials have a coefficient of determination (R2) of greater than 0.984. The maximum change capacitance can be utilized to estimate the various failure depth in asphalt materials. The UTT method makes it difficult to determine the shapes and sizes of various failures in asphalt materials. But the spatially resolved capacitance measurement can determine the various failure shape details of asphalt materials. And the errors in the reconstructed results with failure shape details inside asphalt materials are less than 11%. The findings of this investigation can help engineers realize intuitive identification and precise location of invisible failures in shallow asphalt layers in bridge deck asphalt pavement using coplanar capacitance technology.

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently a promising and leading area of research for component repair and restoration. The Issues of high cost and time consumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover, the potential challenges in dealing with complex components for repair and restoration in the (AM) domain require the establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimization method must cover all important parameters for the complex configuration of structural components under restoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration of monitoring. This improvement is based on facilitating the real-time identification of failures with accuracy and giving a clear monitoring vision according to the intended targets like geometric distortions, residual stresses evaluation, and defect characterization. The improvement involves overcoming a number of challenges such as the pre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with some options that improve the current procedure. Also, this study will conclude and suggest a further framework and new knowledge for restoration and product life cycle extension. This developed ANN can be used at the real pace of modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN. This model development using a neural network has attained a good manipulation of AM. The predicted data from ANN model that was determined and achieved in this study can be used to facilitate and enhance any further study as base knowledge in merging the ANN with another AI to form a hybrid algorithm.

Interface failure identification of argon/nitrogen double plasma modified hybrid laminated para‐aramid composites

AbstractThis work reveals the synergistic effect of argon/nitrogen double plasma alteration on the mechanical properties of para‐aramid fiber/epoxy resin composites with various laminating sequences. Acoustic emission monitored interface failure during tensile, bending and fracture testing. A comparative analysis was conducted on the bending performance of the composites modified by argon/nitrogen double plasma and single plasma. The results showed that the synergistic effect of nitrogen and high‐energy molecule argon optimized the modifying effect of argon/nitrogen double plasma. The optimal modification parameters obtained through fiber extraction testing analysis are power 200 W, time 120 s, and Ar/N2 double gas flow rate of 12 sccm. After plasma treatment, twill weave modifies better than plain weave. The AE signal amplitude of PX1 is minimized during stretching. Due to the difference in internal bending stiffness of PX2, interface delamination failure is more likely to occur. The PX5 had greater fiber and matrix debonding failure between mode‐II fracture tests due to the weak modification effect of plain weave and its difficulty in deformation. In ballistic testing, PX1 blocks impact force through fiber bundle stretching deformation, while PX4 is more brittle.Highlights Prepare para‐aramid/epoxy resin composites with various laminating sequences. Study on the interfacial properties of composites treated with double plasma. The composites with different gas sources treatment were analyzed. Acoustic emission analysis was used to identify interface failure modes. The bonding strength between plain and plain interfaces is the lowest.

Frequency-Shift Monitoring of Optical Filter Based on Optical Labels over FTN-WDM Transmission Systems

Optical network monitoring and soft failure identification such as optical filter shifting and filter tightening are increasingly significant for the complex and dynamic optical networks of the future. Center frequency shift of optical filtering devices in optical networks has a serious impact on the performance of multi-span transmission, especially in high spectrum efficiency faster-than-Nyquist (FTN) transmission systems with various optical switching and add/drop nodes. Existing monitoring schemes generally have the problems of high cost, high complexity, and inability to realize multi-channel online monitoring, which makes it difficult for them to be applied in a wavelength division multiplexing (WDM) system with numerous nodes. In this paper, a monitoring scheme of frequency shift of optical filtering devices based on optical label (OL) is proposed and demonstrated. The signal spectrum of each channel is intentionally divided into many sub-bands with corresponding optical labels loading. The characteristics of spectrum power changing caused by frequency shift can be reflected on labels power changing of each sub-band, which are used to monitor and estimate the value of frequency shift via DSP algorithm. Simulation results show that the monitoring errors of frequency shift can be kept reasonably below 0.5 GHz after 10-span WDM transmission in FTN polarization multiplexing m-ary quadrature amplitude modulation (PM-mQAM) systems. In addition, 250 km fiber transmission experiments are also carried out, and similar results are obtained, which further verify the feasibility of our proposed scheme. The characteristics of low cost, high reliability, and efficiency make it a better candidate for practical application in future FTN-WDM networks.

A Systematic Mapping of the Proposition of Benchmarks in the Software Testing and Debugging Domain

Software testing and debugging are standard practices of software quality assurance since they enable the identification and correction of failures. Benchmarks have been used in that context as a group of programs to support the comparison of different techniques according to pre-established parameters. However, the reasons that inspire researchers to propose novel benchmarks are not fully understood. This article reports the investigation, identification, classification, and externalization of the state of the art about the proposition of benchmarks on software testing and debugging domains. The study was carried out using systematic mapping procedures according to the guidelines widely followed by software engineering literature. The search identified 1674 studies, from which, 25 were selected for analysis. A list of benchmarks is provided and descriptively mapped according to their characteristics, motivations, and scope of use for their creation. The lack of data to support the comparison between available and novel software testing and debugging techniques is the main motivation for the proposition of benchmarks. Advancements in the standardization and prescription of benchmark structure and composition are still required. Establishing such a standard could foster benchmark reuse, thereby saving time and effort in the engineering of benchmarks for software testing and debugging.

Vertex-connectivity for node failure identification in Boolean Network Tomography

We study the node failure identification problem in undirected graphs by means of Boolean Network Tomography. We argue that vertex-connectivity plays a central role. We prove bounds on the maximum number of simultaneous node failures that can be identified in arbitrary networks. We argue that (augmented) grids are a class of networks with large failure identifiability, and provide very tight results in this context.

Improving Productivity Strategies With Failure Mode And Effect Analysis And Analytic Hierarchy Process Methods

UD Fafa Mutiara is a business entity that produces petis as its main product. Products that are considered defective or cannot be repaired and recycled again, will be detrimental to business actors. It is estimated that in each product there are an average of 70 plastic products that are damaged, defective, or even fail, out of 5,000 plastics every month. In a day, Mrs. Ulfa can produce around 175 plastic cases which must be marketed the next day. The purpose of this research is to find out strategies that can improve the quality of UD Fafa Mutiara's petis. One way to find several problems in failure during the petition manufacturing process is known as the Failure Mode and Effect Analysis method, which is abbreviated as FMEA. This method is used to identify potential failures that may occur in the design, production process, or production results. AHP (Analytical Hierarchy Process) has a meaning as a functional hierarchy with the main input being human presumption, which has many advantages in explaining the decision-making process. The research results obtained were that the highest level of risk in the paste production process was when washing raw materials using dirty water with an RPN value of 448. The strategy of using AHP resulted in suggestions for improvements with the highest value, namely checking water with a value of 0.366. So the recommendation that can be given to UD Fafa Mutiara is to maintain the quality of the water used for washing raw materials.
 Highlight :
 
 Identification of potential failures: FMEA method helps identify possible failures in design and production processes.
 Decision-making process: AHP involves human input to explain decision-making processes effectively.
 Risk mitigation strategy: Maintaining water quality during raw material washing is crucial for improving product quality.
 
 Keywords - Petis; production defects; Failure Mode And Effect Analysis (FMEA); Analytic Hierarchy Process (AHP)

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

Improvement in Identification Accuracy of a Failure Diagnostic System for a Reusable Rocket Engine

As a technology for safe and efficient operation of reusable rockets, we are developing failure diagnosis technology for reusable rocket engines. In order to follow the changes in rocket engine operating conditions, a failure diagnostic method which monitors an error vector: the difference between the predicted and measured values of the sensors was developed. The method contains anomaly detection by Mahalanobis distance and failure identification by support vector machines (SVMs). In this report, combinations of monitoring sensors of SVMs for each failure mode were optimized by using design of experiments. By using optimal sensor combinations, the F-score of SVMs were improved in all failure modes. From the results of the orthogonal table experiments, it was supposed that sensors which show the difference in failure modes are important to distinguish failure modes. In addition, a failure classifier combined with the optimized SVMs for each failure mode was developed and demonstrated. The performance of the combined failure classifier with the optimal sensor combination was mostly greater than that with all sensors. However, degradation of the classification performance was also obtained. It is necessary to consider how integrate the results of SVMs which are optimized individually.

Lessons Learned from Aircraft Component Failure Prediction using Full Flight Sensor Data

Successful aircraft predictive maintenance relies on the accurate prediction of major aircraft component failures for operators to schedule and carry out maintenance operations before failure actually happens. In this paper, we share important lessons learned from our development of prognostics alerts using full flight sensor data, including various challenges of using big data, data quality issues, failure identification for data labeling, engineering-driven vs. data-driven methods, and aggregating alerts into actionable alerts. We also provide recommendations based on our experience with prognostic alerts developed and deployed for many airline operators.

Real-Time Cascading Failure Risk Evaluation With High Penetration of Renewable Energy Based on a Graph Convolutional Network

When renewable energy penetration increases and the power network becomes more complex, vulnerability and security concerns typically also become more prevalent. A proper evaluation method focused on risk prediction and failure identification is required in modern power systems. In our work, a novel evaluation framework with high efficiency and accuracy is proposed, where the influence of over/under-voltage on renewable energy generators is considered during the cascading failure risk evaluation process. A graph convolutional network and a long short-term memory network are applied to describe the complex network topological structure and extract the system's electrical features, respectively. The neural network model trained with offline simulation data can reduce the numerical effort of evaluating cascading failure risk in real-time. Finally, the proposed method is verified based on the benchmark of both an IEEE 39-bus test system and a real-world French power system. The results show that the proposed cascade neural network model can achieve better evaluation results with shorter training and evaluation times.

Застосування методу парирування відмов датчиків при управлінні роботою малорозмірного авіадвигуна

The subject of the research is the application of the method of parrying sensor failures - one of the modern progressive approaches to ensuring the smooth and efficient operation of complex automatic control systems. This article is devoted to the application of this method in the control of a small-sized aircraft engine, which is an actual solution for the development of the aviation industry. The purpose of this study is to choose the optimal method for parrying sensor failures of a small-sized aircraft engine by analyzing existing methods and choosing engine parameters that should be used for parrying. Tasks. Consider the main methods of countering sensor failure and justify the choice of the most effective one. To state the main principles of the method of replacing the parameter value at all stages of the development of the control unit for the operation of a small-sized aircraft engine and to carry out an analysis of the disadvantages and advantages of such an approach. Consider algorithms for identifying and countering sensor failures for the engine management system. Analyze the simulation of individual parameters of a small-sized aircraft engine and propose parameters that should be replaced in the event of sensor failure. Research methods. Comparative analysis of methods of parrying sensor failures and models for simulating the parameters of small-sized aircraft engines with adjacent ones. The results. The method for replacing the parameter value with an adjacent one, switching to a backup value, redundancy, and the use of backup data sources are analyzed. Among the parrying methods, the two most optimal ones are selected (the method of replacing the value of the failed sensor parameter with an adjacent parameter and the method of switching to a backup value) and their work algorithms are described. The advantages are increased reliability and efficiency of engine operation, prevention of information loss, adaptability, and improved diagnostics. Among the shortcomings, the existence of risks of erroneous measurements, complex settings, accuracy limitations, dependence on other parameters, cost and complexity of the system are highlighted. Using the example of the ECU-35 unit developed by JSC "Element", the model of replacing the engine rotor speed parameter using the current temperature of the gases behind the turbine, taking into account the calibration of the measurement channel, was described. Conclusions (scientific and practical novelty). It was found that the application of the method of identification and parrying of sensor failures ensured the smooth operation of the control system when individual sensors failed. Thanks to the method for parrying sensor failures, it was possible to achieve a significant improvement in the performance of a small-sized aircraft engine.

Multi-Sensor Fusion for Proactive Anomaly Detection in Robot Navigation

Mobile robots frequently exhibit abnormal behaviors that can impair navigation despite the rapid progress of navigation algorithms. Modern robots need to be able to recognize these unusual behaviors in order to reach high levels of autonomy. Methods for reactive anomaly detection Detect anomalies poor task executions based on the current state of the robot and thus lack the capability to warn the robot before a malfunction actually happens. Due to the possibility of harm to the robot and the environment, such a warning delay is undesirable. For robot navigation in unstructured and uncertain situations, we suggest a proactive anomaly detection network (PAAD). Based on the anticipated movements from the predictive controller and the current observations from the perception module, PAAD forecasts the likelihood of future failure. Effective fusion of multi-sensor signals to provide reliable anomaly detection as seen in the field when there is sensor occlusion environments. Our tests on data from field robots show that our model can catch abnormal actions in real-time while retaining a low false detection rate in congested areas, outperforming earlier methods in failure identification IndexTerms - Face recognition, bias, fairness, soft-biometrics, analysis, privacy, biometrics

Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification

Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to reconstruct tensor feature information based on a three-dimensional matrix for time, frequency, and spatial vectors. A multi-scale wavelet analysis was used to transform the original multi-channel experimental data acquired from a gearbox into a three-dimensional feature matrix of the multi-level structure. The optimal correspondence among the two-dimensional feature signals in the frequency and time domains for the different fault modes was established by the PARAFAC theory. An intelligent APSO algorithm was developed to obtain the optimal parameter structures of an SVM classifier. A comparison with the existing time–frequency analysis method showed that the proposed hybrid PARAFAC-PSO-SVM diagnosis model effectively eliminated the redundant information in the multi-dimensional tensor features but retained the important components. The PARAFAC-APSO-SVM hybrid diagnostic model achieved fast, accurate, and simple fault-classification and identification results, and could provide theoretical support for the application of the PARAFAC theory to complex mechanical fault diagnosis.

Reliability Study of an Intelligent Profiling Progressive Automatic Glue Cutter Based on the Improved FMECA Method

This study introduces the fuzzy theory approach as an enhancement to the traditional failure mode, effect, and criticality analysis (FMECA) method in order to address its limitations, which primarily stem from subjectivity and a lack of quantitative analysis. The proposed method, referred to as FMECA improvement based on fuzzy comprehensive evaluation, aims to quantify the qualitative aspect of the analysis and provides a detailed outline of the analysis procedure. By applying the enhanced FMECA method to assess the reliability of an intelligent profiling progressive automatic rubber cutter, the hazard ranking for each failure mode of the cutter can be determined, thereby identifying areas that require reliability improvement. The analysis outcomes demonstrate that this method establishes a theoretical foundation for subsequent cutter improvement designs, enables early identification of potential failures, and consequently leads to a reduced failure rate and an enhanced reliability level for the intelligent profiling progressive automatic cutter. Furthermore, this innovative agricultural equipment reliability analysis and testing approach holds significant value in elevating the reliability standards of agricultural equipment as a whole and can be explored and implemented in other agricultural machinery contexts.

Integrating Deep Learning with DevOps for Enhanced Predictive Maintenance in the Manufacturing Industry

This research paper explores the transformative integration of Deep Learning (DL) with DevOps methodologies to enhance predictive maintenance in the manufacturing industry. Traditional maintenance strategies often lead to inefficiencies, increased downtime, and operational disruptions. Leveraging the analytical capabilities of DL models and the agile principles of DevOps, our study introduces a comprehensive framework aimed at proactive identification and mitigation of equipment failures. The materials and methods encompass data collection from diverse sources, including sensor data and historical records, coupled with preprocessing techniques to ensure data quality. Selecting appropriate DL models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), enables accurate predictions of equipment failures. The integration pipeline follows DevOps principles, encompassing continuous integration, automated testing, and continuous deployment. Real-time monitoring and feedback mechanisms ensure model adaptability to evolving operational conditions. Collaboration between data scientists, software engineers, and maintenance teams facilitates a holistic approach to system integration. Addressing challenges of collaboration, model drift, and security considerations, our framework lays the foundation for streamlined, efficient, and adaptive predictive maintenance systems. As manufacturing industries embrace digital transformation, the integration of DL with DevOps emerges as a cornerstone for operational excellence, optimizing asset reliability, and contributing to the sustainable evolution of manufacturing ecosystems.