Cause System Failure Research Articles

ENSURING THE FORMATION OF CONTROL ACTIONS FOR CONTROLLING COMPUTER NODE BASED ON PROJECTION ALGORITHMS OF STOCHASTIC APPROXIMATION

In modern conditions, it is important that the implementation of the planned goals leads to the formation of the planned financial and administrative conditions, even in the case of the risk of unauthorized changes in computer systems and environments. There is a need to develop and implement intrusion detection systems that can collect data from various sources within computer systems and networks they protect. Operating systems have system logs, entries in system logs serve as a basis for analyzing the various causes of system failures, identifying and debugging them. Computer attacks are becoming more sophisticated, decreasing efficiency makes it difficult to use traditional anomaly detection methods, so new ones are required. These new approaches and methods are usually based, among other things, on the analysis of data obtained from the system log. After attacks, as more and more components fail, the state of the computer system gradually deteriorates, while number of resources available to perform tasks decrease. Therefore, in this study we consider control of computer nodes based on linear semi-Markov model with changes in the input stream of requests. The approach is based on the use of semi-Markov models of complex systems and analytical nodal modeling. A linear semi-Markov model is offered to describe behavior of a computer node with changes in the input stream of requests. Due to this model, estimates of the temporal functional characteristics for various operation modes of a computer node can be obtained. On its basis, it is possible to provide a response to unauthorized intrusions and, to some extent, compensate for them.

Optimal perfect corrective maintenance policy for a system with multiple components using data‐driven decision‐making methods

AbstractThis paper considers a multicomponent series system to be inspected periodically. It is assumed that the exact cause of system failure might be unknown, and we only know that it belongs to a subset of all components, called masked set. In this case, it is said that the exact cause of failure is masked. A perfect corrective maintenance policy is applied to repair the system, such that a failed component is replaced by a new one at the first inspection time after failure. The distance between each two inspections is considered a decision parameter that should be optimized. Three levels of information availability are considered, including full, partial, and noninformation availability, in which the maintenance policy should be optimized. The former is considered a benchmark, and the statistical and machine learning data‐driven algorithms are used to estimate the cost for the other two information levels. Eventually, using Monte Carlo simulation studies, the proposed method application is analyzed.

Dynamic evaluation of technological innovation system; the case of underground natural gas storage technology in Iran

The technology development is often a process that requires government guidance and support. The government prepare and implement policies for the development of priority technologies. Prior to formulating the policies for technology development, the innovation system should be evaluated and analyzed to identify problems and shortcomings in the system. In the next step, appropriate policies should be implemented to address them. The present study seeks to understand the current situation of the natural gas underground storage technology innovation system in Iran. This innovation system provides a model for evaluating it in Iran by utilizing the framework of the technological innovation system and the method of dynamic evaluation (motors of innovation). Based on that, the system failures in the country have been identified. According to the findings, most important causes of system failure in the development of this technology include insufficiency and low share of private companies in this field, relatively low and average quality of entrepreneurs, and the limited number of storage tanks.

Evaluasi Penyediaan Air Bersih Dengan Sumber Listrik Tenaga Surya Untuk Fasilitas Umum Desa Pasir Panjang

In 2018, Batam State Polytechnic has carried out community service in the village of Pasir Panjang, Rempang Island. Pasir Panjang village is one of the villages that is still not electrified by the local electricity company. The village also experiences difficulties in providing clean water because of its location on the coast. Therefore, the dedication of the Department of Electrical Engineering at the State Polytechnic of Batam in the form of grants and installation of water pumps for public facility with an independent power source from a 100WP solar cell. The problem arises when after carrying out the installation, the water pump cannot pump water properly. In the following year, the service was continued by making improvements to the electrical installation system. Starting from the analysis of the causes of system failure to repair. From the analysis and observation, it is known that the cause of the pump not working properly is due to a decrease in voltage because the Rs value in the solar cell system is quite large. By changing the cable size for current distribution, this problem can be resolved.

Health Indicator Construction Method of Bearings Based on Wasserstein Dual-Domain Adversarial Networks Under Normal Data Only

Rolling bearings are the most critical parts of rotating machinery and their damage is the leading cause of system failures. To ensure the reliability of the system, it demands to construct a health indicator (HI) to assess the state of degradation. However, existing HI construction methods (HICMs) have two limitations. First, the integration of well-designed features relies heavily on the experience of domain expert knowledge. Second, the construction of intelligent HI relies too much on life-cycle data. To cope with these limitations, this article proposed an HICM–Wasserstein dual-domain adversarial networks (WD-DAN), namely HICM-WD-DAN, which can extract generalized features with only normal data during the training. The dual-domain restriction of regularization promotes the generated signals approach to normal samples, making the constructed HI more robust and accurate. Moreover, to balance the weights of dual-domain parts automatically, an independent weighting structure is introduced. Finally, considering the actual degradation state of the system, the modified monotonicity and trendability indexes are proposed to evaluate the performance of HI. The effectiveness of HICM-WD-DAN is verified by bearings’ life-cycle data, and the results show that the constructed HI can represent the irreversible degradation process of bearings accurately and monotonously.

Layer‐wise contribution‐filtered propagation for deep learning‐based fault isolation

AbstractDeep learning is gradually mainstreaming into data‐driven methods, relying on the advantages of extracting complicated nonlinear features. However, the black‐box property makes its decision rules non‐transparent, resulting in difficulty in attribution tasks, which aim to backtrack the contribution of network inputs to the outputs. Fault isolation and localization are techniques for diagnosing the root cause of system failures, which have a consistent objective with attribution for a deep learning‐based fault observer or classifier. Unfortunately, most fault isolation methods are based on shallow learning methods. Also, many attribution algorithms are linear without considering the influence of nonlinear activation functions. The related concerns motivate us to propose a new approach, namely layer‐wise contribution‐filtered propagation (LCP), for deep learning‐based fault isolation. In LCP, reasonable contributions are defined based on the influence of each layer input on maximizing the absolute output activation. A symbolic function is designed to identify neurons with negative contributions, which are then filtered and forbidden to backpropagate to the previous layer. By guiding correct attribution, LCP is available for any nonlinear activation functions and their combinations. It also provides a solution for fault isolation with stacked sample inputs, in which one single variable has several attributions associated with different times. Finally, two chemical simulations verify the effectiveness of the proposed method.

Parameter Estimation Based on Censored Data under Partially Accelerated Life Testing for Hybrid Systems due to Unknown Failure Causes

In general, simple subsystems like series or parallel are integrated to produce a complex hybrid system. The reliability of a system is determined by the reliability of its constituent components. It is often extremely difficult or impossible to get specific information about the component that caused the system to fail. Unknown failure causes are instances in which the actual cause of system failure is unknown. On the other side, thanks to current advanced technology based on computers, automation, and simulation, products have become incredibly dependable and trustworthy, and as a result, obtaining failure data for testing such exceptionally reliable items have become a very costly and time-consuming procedure. Therefore, because of its capacity to produce rapid and adequate failure data in a short period of time, accelerated life testing (ALT) is the most utilized approach in the field of product reliability and life testing. Based on progressively hybrid censored (PrHC) data from a three-component parallel series hybrid system that failed to owe to unknown causes, this paper investigates a challenging problem of parameter estimation and reliability assessment under a step stress partially accelerated life-test (SSPALT). Failures of components are considered to follow a power linear hazard rate (PLHR), which can be used when the failure rate displays linear, decreasing, increasing or bathtub failure patterns. The Tempered random variable (TRV) model is considered to reflect the effect of the high stress level used to induce early failure data. The maximum likelihood estimation (MLE) approach is used to estimate the parameters of the PLHR distribution and the acceleration factor. A variance covariance matrix (VCM) is then obtained to construct the approximate confidence intervals (ACIs). In addition, studentized bootstrap confidence intervals (ST-B CIs) are also constructed and compared with ACIs in terms of their respective interval lengths (ILs). Moreover, a simulation study is conducted to demonstrate the performance of the estimation procedures and the methodology discussed in this paper. Finally, real failure data from the air conditioning systems of an airplane is used to illustrate further the performance of the suggested estimation technique.

Qualitative Analysis of State/Event Fault Trees Based on Interface Automata

State/Event Fault Tree (SEFT) can be used for safety modeling and assessment. However, SEFT does not provide adequate semantics for analyzing the minimal scenarios leading to system failures. In this paper, we propose a novel qualitative analysis method for SEFT based on interface automata. Firstly, we propose the concept of guarded interface automata by adding guards on interface automata transitions. Based on this model, we can describe the triggers and guards of SEFT simultaneously. Then, a weak bisimilarity operation is defined to alleviate the state space explosion problem. Based on the proposed guarded interface automata and the weak bisimilarity operation, the semantics of SEFT can be precisely determined. After that, a qualitative analysis process is presented on the basis of the formal semantics of SEFT, and the analyzing result is the minimal cut sequence set representing the causes of system failures. Finally, a fire protection system case study is illustrated step by step to demonstrate the effectiveness of our method.

Opportunistic perfect preventive maintenance policy in presence of masked data

In this paper, the maintenance optimization problem of multi-component system is considered. It is assumed that the exact cause of system failure might be masked. That is, the exact cause of failure is unknown, and we only know that it belongs to a set called mask set. Both opportunistic perfect preventive maintenance (OPPM) and perfect corrective maintenance are considered. Threshold of OPPM and inter-inspection interval are considered as decision parameters which are optimized using long-run cost rate criteria. The applicability of the proposed maintenance policy is investigated using an illustrative example.

A Failure Analysis Method Based on the Combination of FTA and GGRA: A Case Study on an Engine

As product structure becomes more and more complex, the fault mode presents a diversified trend, and it is more difficult to determine the causes of system failure for a complex product. The main objective of this study is to provide an effective failure analysis method based on the combination of fault trees analysis (FTA) and generalized grey relation analysis (GGRA) for complex product. In this method, the product system failure is defined and the fault tree is constructed by FTA methodology firstly; and then GGRA is employed to identify the correlations between each fault mode and the system failure; finally, the main causes of system failure are identified and the corresponding measures can be made. A case study of a WD615 Steyr engine is conducted throughout the text to verify the validity of this method. The present study would help facilitate the failure and reliability analysis for complex product and benefit designers for the product improvement.

Joint Modeling of Degradation and Lifetime Data for RUL Prediction of Deteriorating Products

Degradation is one of the major root causes of system failure. In some applications, the degradation levels are different upon failure, in which the fixed failure threshold assumption commonly adopted in the degradation literature may not hold. This article tackles the difficulty by jointly analyzing the system degradation and the lifetime data, which enables the corresponding remaining useful life (RUL) prediction. We treat the degradation level as a multiplicative time-varying covariate of the system hazard rate, where a random-effects Wiener process is adopted to model the degradation process. The model parameters are estimated under a Bayesian framework, and we also develop a particle filter method to update the estimates when new data are available. This makes the proposed model be able to realize online RUL prediction based on the in-situ system health state signals. Through case studies on lead-acid batteries and digital communication systems, the proposed model is shown to outperform existing methods in terms of the RUL prediction accuracy.

Software Engineering for Infrastructure and Configuration (SEConfig) - Workshop Report

Errors in con gurations, rather than source code, have become one of the major causes of system failures, resulting in security vulnerabilities, application outages, and incorrect program execu- tions. We report on the structure and results of the rst Interna- tional Workshop on Software Engineering for Infrastructure and Con guration Code. Our aim in organizing this jworkshop was to

E‐Bayesian and hierarchical Bayesian estimations for parallel system model in the presence of masked data

SummaryIn this paper, we consider the statistical analysis of parallel system with inverse Weibull distributed components. Due to cost and time constraints, the causes of system failures are masked and the type‐II censored observations might occur in the collected data. Under the symmetric and asymmetric loss functions, the expected Bayesian (E‐Bayesian) method and the hierarchical Bayesian method are proposed to estimate the parameters, as well as the reliability function. Numerical simulations using the Monte Carlo (MC) method are given to demonstrate the performances of the estimations under different masking levels and effective sample sizes. Finally, one data set is analyzed for illustrative purpose.

Reliability assessment of system under a generalized cumulative shock model

Reliability assessment of system suffering from random shocks is attracting a great deal of attention in recent years. Excluding internal factors such as aging and wear-out, external shocks which lead to sudden changes in the system operation environment are also important causes of system failure. Therefore, efficiently modeling the reliability of such system is an important applied problem. A variety of shock models are developed to model the inter-arrival time between shocks and magnitude of shocks. In a cumulative shock model, the system fails when the cumulative magnitude of damage caused by shocks exceed a threshold. Nevertheless, in the existing literatures, only the magnitude is taken into consideration, while the source of shocks is usually neglected. Using the same distribution to model the magnitude of shocks from different sources is too critical in real practice. To this end, considering a system subject to random shocks from various sources with different probabilities, we develop a generalized cumulative shock model in this article. We use phase-type distribution to model the variables, which is highly versatile to be used for modeling quantitative features of random phenomenon. We will discuss the reliability characteristics of such system in some detail and give some clear expressions under the one-dimensional case. Numerical example for illustration is also provided along with a summary.

Risk Based Technology Qualification Method for HPHT Well

With the increasing demand for new technologies in HPHT (high pressure high temperature) wells, more and more attention has been paid to the system of technical qualifications. Most established norms, standards and recommended practices are written in prescribed formats, setting out methods, procedures and acceptance criteria for product design, manufacture and testing. However, established norms, standards or recommended practices may not adequately cover new technologies or applications. The technical qualification system is established in this paper, which can identify the potential failure modes and some activities in order to control the uncertainties and increase confidence of using new technology. A risk-based technical qualification system is used to assess non-traditional operations. It includes following seven main steps: system breakdown, technology assessment and classification, technical novelty, failure model and risk analysis, risk-based qualification planning and participation level development. The core of technical qualification system is to understand the ways and causes of system failure, and to carry out corresponding activities to provide evidence that the system will not fail. Through the technical identification process, the detailed equipment/operation specifications and identification requirements have been formulated. Different levels of participation are assigned for each eligibility activity, depending on the hazard and risk. It is proved that the application of the technical qualification principle in the systematic evaluation and control of the risks of new technologies or new applications is successful. In HPHT wells in particular, operators can move from a reactive approach to a proactive approach.

Preliminary Hazard Analysis dan Fault Tree Analysis untuk Identifikasi Penyebab Kegagalan Sistem Pelumas Mesin Induk Kapal Penangkap Ikan

Kapal penangkap ikan di Indonesia sebagian besar menggunakan mesin diesel sebagai tenaga penggeraknya. Mesin diesel merupakan mesin mekanis dimana singgungan komponen tidak dapat dihindari, sehingga sistem pelumas mutlak diperlukan untuk mengurangi tingkat kerusakan komponen yang bersinggungan. Penurunan kemampuan sisteem pelumas berdampak pada kerusakan mesin. Tujuan penelitian ini untuk mengidentifikasi penyebab kerusakan sistem pelumas mesin diesel kapal penangkap ikan sehingga memudahkan untuk membuat keputusan dalam melakukan tindakan perawatan dan perbaikan sistem pelumas. Metode untuk mengidentifikasi tiap kegagalan yang mungkin terjadi menggunakan Preliminary Hazard Analysis (PHA) dan Fault Tree Analysis (FTA). PHA merupakan perangkat untuk mengidentifikasi potensi bahaya yang mengakibatkan kegagalan sistem, FTA merupakan perangkat untuk mengidentifikasi penyebab kegagalan sistem. FTA bekerja dengan menentukan kegagalan yang disebut sebagai “top event”, selanjutnya top event diidentifikasi hubungan faktor penyebab berbagai peristiwa kegagalan dengan pohon kesalahan yang menggunakan gerbang logika sederhana “and” dan “or”. Terbentuknya FTA dapat menentukan faktor penyebab kegagalan dengan minimal cut set. Berdasarkan hasil analisis, terdapat tiga penyebab utama mesin diesel mengalami kegagalan karena sistem pelumas adalah gagalnya komponen sistem pelumas yaitu Kebocoran internal, (Screen pelumas + alarm tekanan + manometer + petugas), dan (Pompa pelumas + alarm tekanan + manometer + petugas).

Applying the Anticipatory Failure Determination at a Very Early Stage of a System’S Development: Overview and Case Study

Abstract Anticipatory Failure Determination (AFD) is a tool used in the TRIZ (Theory of Inventive Problem Solving) methodology. This article introduces its concept and describes the process of AFD in different versions of the method. The article presents the application of the AFD method at a very early state of a system’s development, i.e. its concept formulation stage, which corresponds to a technology readiness level (TRL) equal to 2. The system under analysis is a set of devices used to reduce displacement ship hull resistance. The system was modelled using functional analysis. An analysis of system resources was then carried out. Possible direct, indirect, and accident-related failures were identified. A multi-criteria analysis of the causes of system failures was conducted from which the top 10 potential failures were selected. Observations were made on the applicability of AFD in respect to systems not yet implemented.

How Good Is My Test Data? Introducing Safety Analysis for Computer Vision

Good test data is crucial for driving new developments in computer vision (CV), but two questions remain unanswered: which situations should be covered by the test data, and how much testing is enough to reach a conclusion? In this paper we propose a new answer to these questions using a standard procedure devised by the safety community to validate complex systems: the hazard and operability analysis (HAZOP). It is designed to systematically identify possible causes of system failure or performance loss. We introduce a generic CV model that creates the basis for the hazard analysis and—for the first time—apply an extensive HAZOP to the CV domain. The result is a publicly available checklist with more than 900 identified individual hazards. This checklist can be utilized to evaluate existing test datasets by quantifying the covered hazards. We evaluate our approach by first analyzing and annotating the popular stereo vision test datasets Middlebury and KITTI. Second, we demonstrate a clearly negative influence of the hazards in the checklist on the performance of six popular stereo matching algorithms. The presented approach is a useful tool to evaluate and improve test datasets and creates a common basis for future dataset designs.

High-speed Train Axle Temperature Monitoring System Based on Switched Ethernet

China railway transportation is developing rapidly, the running speed of high-speed train is promoted. As the promoted speed, the running gear and gearbox of high-speed train produce more heat, causing hot axle easily. In view of the hot axle phenomenon of high-speed train during operating, in combination with the former failure condition of axle temperature monitoring system in terms of total composition, principle and failure condition during operating, analyzing the causes of system failure deeply, a new type of axle temperature monitoring system is designed. Through the test, it indicates that the system could monitor the axle temperature condition of high-speed operating train real-time and accurately, it plays an important role in ensuring the safe operation of high-speed train.

Risk assessment techniques with applicability in marine engineering

Nowadays risk management is a carefully planned process. The task of risk management is organically woven into the general problem of increasing the efficiency of business. Passive attitude to risk and awareness of its existence are replaced by active management techniques. Risk assessment is one of the most important stages of risk management, since for risk management it is necessary first to analyze and evaluate risk. There are many definitions of this notion but in general case risk assessment refers to the systematic process of identifying the factors and types of risk and their quantitative assessment, i.e. risk analysis methodology combines mutually complementary quantitative and qualitative approaches. Purpose of the work: In this paper we will consider as risk assessment technique Fault Tree analysis (FTA). The objectives are: understand purpose of FTA, understand and apply rules of Boolean algebra, analyse a simple system using FTA, FTA advantages and disadvantages. Research and methodology: The main purpose is to help identify potential causes of system failures before the failures actually occur. We can evaluate the probability of the Top event.The steps of this analize are: the system's examination from Top to Down, the use of symbols to represent events, the use of mathematical tools for critical areas, the use of Fault tree logic diagrams to identify the cause of the Top event. Results: In the finally of study it will be obtained: critical areas, Fault tree logical diagrams and the probability of the Top event. These results can be used for the risk assessment analyses.