Reliability Of Engineering Systems Research Articles

Application of Hierarchical Colored Petri Nets for Technological Facilities’ Maintenance Process Evaluation

The high reliability of modern engineering systems is achieved by performing predictive maintenance. Mathematical models based on stochastic timed colored Petri nets are an effective tool for developing complex production processes for Industry 4.0. This article discusses the maintainability evaluation used in hierarchical Petri net models. The hierarchical simulation model was built using timed colored Petri nets, and was constructed with four levels of repair and maintenance modules. New module structures are proposed for simulating the schedule of production tasks and interaction with technological units. The emphasis is on the processes of predicting maintenance and repair, moving units to service, replacing units, and forming a reserve. The design of the simulation modules allows the setting of probabilistic parameters for the distributions of equipment failures, requests for unit maintenance, repair time, and recovery time after repair. The article proposes to use the hierarchical Petri model in conjunction with solving the problem of minimizing the cost of service. The iterative procedure consists of obtaining an approximate unit distribution by tasks, subsequent simulation of the technological process, and adjusting the optimization problem constraints. For example, the hierarchical Petri net is considered to assess the maintainability of autonomous agricultural vehicles. The results of the simulation experiments are presented. A simulation of the agrotechnical production process was performed, during which vehicles were maneuvered, taken out for repair or maintenance, and returned to the reserve fund. The interdependencies of preventive maintenance periods, service operations, failure rates, and predictive maintenance requests were obtained in order to comply with the task scheduling. The proposed model is a generalization, but it is especially effective in studying mobile equipment servicing.

Autoencoder Quasi-Recurrent Neural Networks for Remaining Useful Life Prediction of Engineering Systems

Remaining useful life (RUL) prediction is a key solution to improve the reliability, availability, and maintainability of engineering systems. Long short-term memory (LSTM) and convolution neural networks (CNN) are the current hotspots in the field of RUL prediction. However, the LSTM-based prognostic approach has a slow loop step to process large-scale time-series data since the dependence of the data processing process at each time on the output of the previous time limits parallelism, and the CNN-based prognostic approach is not fit for time-series data although it can process the data in parallel. In this article, a new autoencoder quasi-recurrent neural networks (AEQRNN) based prognostic approach is proposed for RUL prediction of the engineering systems. The AEQRNN contains convolution components that can process input data in parallel, and pooling components which has two LSTM-like gate structures to process time-series data. In addition, the AEQRNN can automatically extract hidden features from monitoring signals without manual feature design. The effectiveness of the proposed prognostic approach is validated by three prognostic benchmarking datasets, including a turbofan engine dataset, a rolling bearing dataset, and a machining tool dataset. Experimental results demonstrate that this approach has both superior prognostic performance and training speed in comparison with other kinds of recurrent-neural-network-based approaches and various state-of-the-art approaches in the recent literature.

OPTIMIZING RELIABILITY OF LINEAR FRACTIONAL DIFFERENCE SYSTEMS UNDER UNCERTAINTY AND RANDOMNESS

Some complex systems may suffer from failure processes arising from soft failures and hard failures. The existing researches have shown that the reliability of a dynamic system is not constant under uncertain random environments. First, two types of uncertain random optimization models are proposed where reliability index is quantified by chance measure based on whether soft and hard failures are independent or not. It is considered that internal degradation is driven by left Caputo fractional linear difference equation, while shocks are defined as discrete i.i.d. random variables. The shocks may generate additional uncertain degradation shifts when considering the competing dependent failure processes. Then, two proposed optimization reliability problems may be transformed into their equivalent deterministic forms on the basis of [Formula: see text]-path, and improved gradient descent method is applied to obtain optimal solutions. Finally, the numerical example of a micro-engine indicates that the optimization models are beneficial to the reliability of engineering systems.

Analysis of Reliability and Safety Issues in Engineering Systems: A Multi-criteria Approach

The current huge investments in engineering systems have created a renewed concern about their reliability and safety among scholars and practitioners. This article bridges the knowledge gap in the evaluation of reliability and safety concerns in engineering systems. It uses a literature review and qualitative content analysis method to identify the underlying factors that cause reliability and safety issues in engineering systems. The identified factors are organizational, environmental, and physical and mechanical factors. An extended Fuzzy Analytical Hierarchy Process (FAHP) method was used to determine rank these factors criticality to accidents occurrence in engineering systems. FAHP’s applicability was demonstrated using cranes as a case study. Based on the analysis of three experts’ evaluations, the environmental factor is the most critical accidents in engineering systems. On the other hand, the organizational factor is the least critical factor to accidents in engineering systems. The study will be beneficial to product development/manufacturing companies. Its findings could provide practitioners and scholars with sound knowledge of the implications of reliability and safety issues on engineering systems accidents.

Generalized matrix-based Bayesian network for multi-state systems

To achieve a resilient society, the reliability of core engineering systems should be evaluated accurately. However, this remains challenging due to the complexity and large scale of real-world systems. Such complexity can be efficiently modelled by Bayesian network (BN), which formulates the probability distribution through a graph-based representation. On the other hand, the scale issue can be addressed by the matrix-based Bayesian network (MBN), which allows for efficient quantification and flexible inference of discrete BN. However, the MBN applications have been limited to binary-state systems, despite the essential role of multi-state engineering systems. Therefore, this paper generalizes the MBN to multi-state systems by introducing the concept of composite state. The definitions and inference operations developed for MBN are modified to accommodate the composite state, while formulations for the parameter sensitivity are also developed for the MBN. To facilitate applications of the generalized MBN, three commonly used techniques for decomposing an event space are employed to quantify the MBN, i.e. utilizing event definition, branch and bound (BnB), and decision diagram (DD), each being accompanied by an example system. The numerical examples demonstrate the efficiency and applicability of the generalized MBN. The supporting source code and data can be download at https://github.com/jieunbyun/Generalized-MBN-multi-state.

Modern Diagnostic Techniques and Mathematical Models of Reaching High Reliability and Safety in Engineering Systems during Design and Tests of Developmental Prototypes

This article is about reaching and securing high reliability and safety in engineering systems during the design and testing of developmental prototypes. A new approach to and technique of calculation of the design reliability is proposed, taking into account both kinds of failure rates, those that are constant and those that change over time. The function for calculation of design reliability is represented by the product of three constituents of failsafe operation probabilities throughout the whole period of operation, considering the calculation of the reliability of mechanical components and metalwork. The calculation model proposed for reaching and securing the required reliability and safety levels takes into account controlling actions. A new approach and technique, as well as mathematical models, for planning the scope of testing for various laws of run-to-failure distribution are proposed on the basis of nondiscardability of expensive objects. The study results are recommended for use in creating complex engineering systems.

Definitions and Reliability Evaluation of Multi-state Systems Considering State Transition Process and its Application for Gas Systems

Multi-state system (MSS) models have been widely used to represent the reliability of engineering systems that have a finite number of performance levels. The previous studies related to the reliability evaluation of the MSSs generally follow the assumption that the transition among different performance states is instantaneous with no time delay. However, there exist several engineering systems that transit slowly between different states, such as gas supply systems and heating distribution systems. When evaluating the operating reliability of such an engineering system, the neglect of the non-instant state transition process may lead to inaccurate or insufficient evaluation results. To fill the research gap, the traditional MSS model is extended to handle the situation where the non-instant state transition features should be considered. The concepts and features of multi-state systems considering state transition process (MSS-STP) are innovatively proposed. Considering the non-instant state transition process, the traditional reliability evaluation framework for the MSSs is extended to evaluate the operating reliability of MSS-STP. The procedure of transition curve discretization is innovatively proposed to divide the dynamic transition process into a series of discrete states. The developed general MSS-TSP model is applied to the natural gas system (NGS) to demonstrate the effectiveness of the proposed reliability evaluation framework. The proposed technique can provide a useful tool for system operators to accurately evaluate the operating reliability of MSS-STP

Medication Safety and the Independent Double Check: A Work System Analysis and Reliability Engineering Theory Review

Double checking is a standard practice in many areas of healthcare, despite the growing body of evidence suggesting that it is hard to do, and ineffective as a safety measure. We ask here: ‘What are the work system barriers that make IDC so ineffective?’ and, ‘What does the literature say about double-checking as a safety measure in sociotechnical systems?’

The Reliability and Lifetime of Engineering Systems in Extreme Operation Conditions

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Robust Incipient Fault Detection of Complex Systems Using Data Fusion

An incipient fault refers to the first change point when a system starts to deteriorate. Early detections of incipient faults are crucial to the safety, reliability, and effective predictive maintenance of complex engineering systems. However, it is very difficult to detect incipient faults at the initial stage of the system-degradation processes. To address this issue, an innovative data-fusion method is introduced to detect the incipient faults by integrating data collected from multiple sources instead of a single data source. The data-fusion problem is formulated as a convex optimization problem, aiming at reducing the false-alarm rate and the time to detect incipient faults. We demonstrate the proposed data-fusion method using a data set generated by the degraded aircraft engines. The numerical results have demonstrated that the proposed method can assist in reducing both the false-alarm rate and the time to detect incipient faults.

An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis

In this paper, the active learning Kriging model (ALK), which has been studied extensively in recent years, has been expanded by combining with the directional importance sampling (DIS) method. The directional sampling method can reduce the dimensionality of the variable space by random sampling or interpolation in the direction of vector diameter, which can improve the efficiency of reliability analysis. It is especially suitable for the surfaces whose limit state is spherical or near-spherical. By introducing the control coefficient and constructing the directional importance sampling density function, the sampling efficiency can be further improved in the design point domain. A novel reliability analysis method called ALK-DIS method is proposed. The greatest advantage of the proposed method is its ability on great computational efficiency and dealing with small failure probability problem In addition, due to the excellent performance of directional sampling method in dealing with multi-failure model reliability problems, the ALK-DIS method has the advantage of being applied to system reliability analysis in this paper successfully. The applicability, feasibility and efficiency of the proposed method are proved on examples which contain linearity equation, non-linear numerical example, non-linear oscillator and system reliability engineering problems.

A clustering approach to detect faults with multi-component degradations in aircraft fuel systems

Accurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%.

Software Engineering Methods to Improve the Design of Software Reliability Systems: Roadmap

The reliability of software is founded on the development, testing, evaluation and maintenance of software systems. In recent years, researchers have been come to see software reliability as a major focus. This is due to the fact that reliability is central to all software quality concepts. System Reliability Engineering is the study of the processes and results of software systems in relation to the basic requirements of users. This paper provides an overview (roadmap) of current developments in software reliability metrics, modeling and operational profiles. It outlines several software engineering methods to achieve reasonable system reliability. Finally, failure metrics are considered based on feedback collected from users after releasing the software and case studies of detected failures. Consequently, numbers and types of failures will be recorded from the users feedback.

A novel methodology of sequential optimization and non-probabilistic time-dependent reliability analysis for multidisciplinary systems

Abstract Various uncertainties, which are usually time-dependent, affect the reliability of complicated engineering systems seriously. Considering the fact that only limited sample data of the uncertain variables can be obtained in engineering practice during the whole in-service time of multidisciplinary systems, the interval process model is introduced to model the time-dependent uncertain variables, and a non-probabilistic time-dependent reliability estimation model is proposed. In addition, a sequential multidisciplinary optimization and non-probabilistic time-dependent reliability assessment (SMO_NTRA) approach is developed to decouple the time-dependent reliability analysis from the multidisciplinary design optimization (MDO). In the framework of SMO_NTRA, the deterministic MDO and non-probabilistic time-dependent reliability analysis are executed in a sequential manner. Thus the computationally expensive double level optimization problem can be avoided and the efficiency can be greatly improved. Furthermore, the shifting distance of the constraint is calculated by bi-section method. Both numerical and engineering examples are employed to demonstrate the validity of the proposed method.

Совершенствование и развитие системы оперативного управления охраной труда и промышленной безопасностью

In modern conditions, the program of a coal enterprise development is governed primarily by the economic factors. Despite the favorable trends, the state of industrial safety in the coal industry remains dissatisfactory, and the anti-accidental stability of the Russian coal enterprises requires improvement. Reliability of functioning of any complex technical system, namely, such as the modern mine seems to be, is determined primarily by human actions. More than 50 % of accidents in the technogenic sphere are caused by the human errors, therefore the decisive role in improving reliability of modern engineering systems belongs to the professional specialization and information provision of the employee operating in the system. Based on the analysis of accidents and injuries rate, the most important tasks are assigned to: improvement of the legislative framework and normative-legal acts in the field of industrial safety; development of the integrated systems for the objective continuous monitoring of technologies and safety parameters; development and implementation in the industrial production of the integrated computer technologies in the field of industrial and occupational safety in the coal industry. State programs for the coal industry development provide for: introduction of the specialized software that allows to avoid emergency situations in the mines; systemic use of the multifunctional safety system in mining operations; management and control of the technological and production processes in both normal and emergency situations. Among these systems of the automated control of production processes is the specialized software package for pocket PC (smartphone, pad) designed for automation control over collection, processing of primary information in the electronic form.

Hybrid multiobjective optimization algorithm based on multivariate mean square error and fuzzy decision maker

This work presents the turning process of AISI H13 hardened steel with the PCBN 7025 tool, considering six output variables: tool life, machining total cost, surface roughness, machining force, sound pressure level, and specific cutting energy. Several problems are encountered in engineering processes that have adverse effects on the reliability of complex engineering systems. Hence, the aim of this work is to optimize the hardened steel turning process by applying mathematical methods to reduce dimensionality and eliminate the correlation between the multiple responses. The resultant latent response surfaces and their respective targets constitute the normalized multivariate mean square error (MMSE) function that is minimized by the normal boundary intersection (NBI) method. Furthermore, a fuzzy algorithm is applied to identify the best solution from several feasible solutions of the Pareto frontier that is compared with the performances of normalized normal constraint, arc homotopy length, global criterion method, and desirability method. The results show that NBI-MMSE has a higher performance than the other methods. In addition, NBI-MMSE is tested with benchmark functions to evaluate its effectiveness and robustness. Therefore, NBI-MMSE identifies the dynamics of the turning process of AISI H13 steel by revealing the optimal solutions for the input process parameters.

An RBMs-BN method to RUL prediction of traction converter of CRH2 trains

Remaining useful life (RUL) prediction is essential to ensure safety and reliability of engineering systems. To achieve better prediction performance, causalities among the physical quantities are considered by applying Bayesian Network (BN) to RUL prediction. For this purpose, several improvements on BN modeling are made in this paper, to handle the closed-loop control structure of engineering systems, and to improve prediction performance with reduced complexity. Taking the traction converter of CRH2 trains as the object of the research, a closed-loop Bond Graph (BG) model is firstly developed to describe the causality of multi-domain physical quantities, which is then transformed to be a BN structure. Then, multi-dimensional features are extracted from the condition monitoring data and are used as the inputs to the nodes of BN model. Finally, Restricted Boltzmann Machines (RBMs) are used to further extract the latent features that cannot be directly observed or measured, but greatly improve the accuracy of the BN based RUL prediction. Case study is conducted using a hardware-in-loop simulation platform for traction system of China Railway High-speed (CRH2) trains, to predict RUL of the DC-link circuit with degradation of capacitance or resistance. The experimental results can show the validity and superiority of the proposed RBMs-BN based RUL prediction method.

Hybrid-Optional Effectiveness Functions Entropy Conditional Extremization Doctrine Contributions into Engineering Systems Reliability Assessments

Abstract In this publication a Doctrine for the Conditional Extremization of the Hybrid-Optional Effectiveness Functions Entropy is discussed as a tool for the Reliability Assessments of Engineering Systems. Traditionally, most of the problems having been dealt with in this area relate with the probabilistic problem settings. Regularly, the optimal solutions are obtained through the probability extremizations. It is shown a possibility of the optimal solutions “derivation”, with the help of a model implementing a variational principle which takes into account objectively existing parameters and components of the Markovian process. The presence of an extremum of the objective state probability is observed and determined on the basis of the proposed Doctrine with taking into account the measure of uncertainty of the hybrid-optional effectiveness functions in the view of their entropy. Such approach resembles the well known Jaynes’ Entropy Maximum Principle from theoretical statistical physics adopted in subjective analysis of active systems as the subjective entropy maximum principle postulating the subjective entropy conditional optimization. The developed herewith Doctrine implies objective characteristics of the process rather than subjective individual’s preferences or choices, as well as the states probabilities maximums are being found without solving a system of ordinary linear differential equations of the first order by Erlang corresponding to the graph of the process. Conducted numerical simulation for the proposed mathematical models is illustrated with the plotted diagrams.

Influence of stakeholder management and communications management in choosing of the best product achieve quality by Analytic Hierarchy Process method

This article investigate an analytical study of the stakeholder management and the communications management of Ready Mix Concrete, its importance and the role to identify, plan, management, engagement and control engagement in process of production to make decision about the best product achieve quality through the Analytic Hierarchy Process method (AHP) to compare criteria and variances pairwise comparison to choose best product meet with the requirements of quality management. The choice (best product) will determine the quality of actions that we can take without losses of costs for development of other products that can consume the time, effort and cost to correct or update its progress in the production process. Our choice of a product that reduces costs will help us to determine the tests and inspections that can achieve and have the most impact on quality (control and assurance). The Stakeholder management and communications management help us to determine criteria and variances as well as management. The author uses Expert Choice software for submitting goals, criteria, pairwise comparison of criteria and as well as submitting of alternatives. Moreover, the study of information base on the matrices of pairwise comparison to the alternatives with respect to cost, meet with standards, experience of functional manager, the reliability of engineering system, quality of human resources, safety and the analysis of the results based on sensitivity criterion, performance sensitivity, dimensional sensitivity, dynamic sensitivity, and the opinion of the experts, the best product achieve quality is Ready Mix Concrete more than other main products.

Sequential optimization and fuzzy reliability analysis for multidisciplinary systems

To meet the rising demand for high reliability in complex multidisciplinary engineering systems, more attention has been paid to reliability-based multidisciplinary design optimization (RBMDO). In this paper, a sequential optimization and fuzzy reliability analysis (SOFRA) method for multidisciplinary systems is developed to decouple the fuzzy reliability analysis from the optimization. In SOFRA, the multidisciplinary design optimization (MDO) and fuzzy reliability analysis are conducted in a sequential manner. Furthermore, a novel adaptive collocation method (ACM) is proposed to conduct the fuzzy reliability analysis for multidisciplinary systems. The ACM arranges points adaptively at the axis of the membership to obtain more accurate results. The shifting distance of the constraint is calculated by the bi-section method. Both numerical and engineering examples are used to demonstrate the validity of the proposed method.