Faulty Components Research Articles

A novel prognostics solution for machine tool sub-units: The hydraulic case

A novel prognostic approach was developed and applied to a machine tool hydraulic unit. Three components were considered: pump, sensor and valve. The proposed methodology exploited a digital twin of the system to perform simulations of the healthy and faulty machine. The digital twin was properly validated through experiments. This approach dealt with the need to carry out time-consuming and expensive experimental campaigns, that is, run-to-failures – not affordable in many industrial applications. The diagnosis module was trained on digital twin simulations and fulfilled the fault detection, isolation and quantification phases. The challenge related to the variability of the operating conditions of the machine was addressed through a robustness analysis of the methodology. The solution successfully dealt with both stationary and non-stationary working conditions. A dedicated classification model was designed for each faulty component, maximising the associated classification rate. The testing procedure consisted of the application of a 10-fold cross-validation to compute the mean classification rates for stationary and non-stationary working conditions. Diagnosis performance results were excellent for the pump, whereas they were lower for the sensor and valve, reaching 79.75% and 74.93% accuracy respectively for the most challenging working cycle. The prognosis directly exploited the output of diagnostics, allowing for experimental effort reduction. Prognosis predictions were built starting from the updated health status provided by the diagnosis output. In order to test the prognosis module, mean and standard deviation of the prediction errors (less than 1.176%) were computed through a Monte Carlo approach. The conceived methodology allowed one of the critical goals of prognostics to be handled: the Remaining Useful Life probability density function estimation.

TreeMerge: Efficient Generation of Minimal Hitting-Sets for Conflict Sets in Tree Structure for Model-Based Fault Diagnosis

For many high-tech fields such as space exploration, nuclear technology, and smart automobiles, it is vital to timely find faulty components of man-made devices to ensure safety. However, there is nearly no enough diagnostic experience accumulated in these new devices, and thus, it is hardly suitable to only apply the traditional expert/experience-based fault diagnosis approach. Thus, model-based diagnosis was proposed for efficient detection of faulty components; this approach explores the behavioral and structural information of the device to be diagnosed, and no experience is required. In model-based diagnosis, for a device to be diagnosed, minimal conflict sets of components are first generated, and all minimal hitting-sets for them will be derived as candidate diagnoses. Therefore, it is vital to efficiently generate all minimal hitting-sets to find the final diagnosis. Unfortunately, it is proven to be NP-hard when deriving all minimal hitting-sets for given minimal conflict sets. To improve the computing efficiency, in this article, we propose a novel approach called TreeMerge , which considers a special type of tree structure of minimal conflict sets of large sizes since structural information usually plays an important role in solving complex problems. Theoretically, compared with other algorithms, the time complexity of the new algorithm is greatly reduced, as the time complexity of the new algorithm becomes linear rather than quadratic . Furthermore, experimental results on multiple synthetic and benchmark examples show that the proposed TreeMerge algorithm is more efficient than many other state-of-the-art methods, with a reduction of several orders of magnitude runtime (seconds).

Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.

Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System under Intermittent Fault

In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.

Statistical and intelligent reliability analysis of multi-layer ceramic capacitor for ground mobile applications using Taguchi’s approach

PurposeAlthough Multi-Layer Ceramic Capacitors (MLCC) are known for its better frequency performance and voltage handling capacity, but under various environmental conditions, its reliability becomes a challenging issue. In modern era of integration, the failure of one component can degrade or shutdown the whole electronic device. The lifetime estimation of MLCC can enhance the reuse capability and furthermore, reduces the e-waste, which is a global issue.Design/methodology/approachThe residual lifetime of MLCC is estimated using empirical method i.e. Military handbook MILHDBK2017F, statistical method i.e. regression analysis using Minitab18.1 as well as intelligent technique i.e. artificial neural networks (ANN) using MATLAB2017b. ANN Feed-Forward Back-Propagation learning with sigmoid transfer function [3–10–1–1] is considered using 73% of available data for training and 27% for testing and validation. The design of experiments is framed using Taguchi’s approach L16 orthogonal array.FindingsAfter exploring the lifetime of MLCC, using empirical, statistical and intelligent techniques, an error analysis is conducted, which shows that regression analysis has 97.05% accuracy and ANN has 94.07% accuracy.Originality/valueAn intelligent method is presented for condition monitoring and health prognostics of MLCC, which warns the user about its residual lifetime so that faulty component can be replaced in time.

Technical assessment of installed domestic biogas plants in Kavre, Nepal

Domestic biogas technology is a promising source of renewable energy for cooking in developing countries. It is popular in Nepal's rural and peri-urban areas and can be scaled-up to replace solid fuel cookstoves. This study focused on the technical assessment of installed biogas plants such as structural components, pipelines and valves, stove and burner, biogas production and utilization, and effluent disposal system in 303 homes in Kavre district, Nepal. Furthermore, it evaluated the feedstock material's physical and chemical parameters to ascertain their implications on biogas production. The plants' problems were mainly due to faulty components and lack of proper operation and maintenance. About 18% of plants were nonfunctional, out of which 9% of the plants were damaged due to the 2015 mega-earthquake. The highest number of damages was found in the mixture of the inlet structure. About 55% of mixtures were nonfunctional. Almost 77% of users used cow dung and toilet waste either individually or in combination with animal urine as feedstock materials in the digester. The Carbon: Nitrogen (C: N) ratio of feedstock materials ranged from 5:1–24:1, with only 4% of samples measuring C: N greater than 20:1. Similarly, pH (7.7) levels were on the higher side.

A Fast Diagnosis Scheme for Multiple Switch Faults in Cascaded H-Bridge Multilevel Converters

The increasing importance of cascaded H-bridge multilevel converters (CHBMCs) in high-power applications requires a higher priority to detect and protect against fault conditions. In this article, a current residual-based diagnosis scheme is proposed for the CHBMCs without additional sensors. On the premise of filtering the sampled dc voltages, comparing the calculated actual values of output current with the expected values in the control system, normalized diagnostic variables are established to localize single or multiple open-circuit switches. Benefit from the diagnostic variables corresponding directly to the switches of each H-bridge, this method can be easily extended to the CHBMCs with arbitrary submodules. Furthermore, the parameter estimation and enhanced detection algorithm are introduced to ensure the immunity of the diagnostic method to the large parameter changes and measurement noises. The faulty component can be accurately diagnosed within one line cycle regardless of the faulty modules and operating conditions. The effectiveness of the proposed method is substantiated using experimental results from the hardware platform of the CHBMCs.

Fault detection and diagnosis in multivariate systems using multiple correlation regression

Correlations among variables are inherent features of a multivariate system. Making a good use of variable correlations is important to fault detection and diagnosis (FDD). In this paper, a novel FDD method is proposed using multiple correlation regression (MCR). MCR builds a regression model using the multiple correlation of a variable with other variables. MCR is the best linear regression that produces residuals with the minimal variance. Based on the MCR residuals of variables, a T2 statistic is defined for fault detection. This T2 statistic consists of a set of independent components, with each component corresponding to the MCR residual of a variable. To reduce the amplification and masking effects caused by fault-free components, three improvements to the residual-based T2 statistic are proposed, called the intersecting T2 (IT), weighted T2 (WT) and enhanced T2 (ET) statistics. In particular, the WT and ET statistics and their control limits vary with samples to adapt to dynamic changes of the system. An online fault detection strategy is proposed by combining the WT and ET statistics. The WT statistic is firstly used to detect the occurrence of faults. After faults were detected, the ET statistic is used to replace the WT statistic in order to enhance the capability of detecting subsequent faulty samples. A contribution-based fault diagnosis method is also developed. Faulty variables are identified by comparing variable contributions to the key faulty component of the residual-based T2 statistic. The implementation and advantages of the proposed FDD method are illustrated with a simulation example and an industrial case study.

Infrared thermography-based condition monitoring of solar photovoltaic systems: A mini review of recent advances

Globally, solar photovoltaic (PV) plants have been in continuous increase, attracting researchers and governments' interest, and PV markets witness high competition. That requires advanced research and development of reliability and efficiency optimization, fault detection and diagnosis, and maintenance of various components, particularly PV modules. Along with that, many works and technical investigations have been conducted via many detection methods based on either indoor or outdoor tests. Specifically, the temperature is a common indicator of any structure components' health as faulty and damaged components, and corroded electrical connections can lead to abnormal temperature distribution. Hence, such faults can be detected through imaging and analyzing temperature distribution patterns localizing the faulty part. For so, Infrared Thermography (IRTG) has become a widely-utilized condition monitoring (CM) technique; through which real-time temperature can be measured. It is regarded as reliable, accurate, and economical method due to the advent of IR cameras. IRTG features for being safe and non-destructive testing technique (NDTT); and hence it has been effectively used in detecting PV plants either in small or large scales. This survey manuscript presents a general view about IRTG and focuses on its utilization in condition monitoring of PV plants merging the state-of-the-art researchers' efforts in this field. In addition, some common patterns of detected faults and their analyses are also illustrated.

Design of the HEBT components inside TIR room of the IFMIF DONES facility

The High Energy Beam Transport (HEBT) line components maintenance are to be conducted during twice yearly shutdown “beam off” periods, one 3-day shutdown and the main “beam off” shutdown period (annual preventive maintenance), which includes the necessary cooling and warming time, all totalling 20 days. Due to the radiation doses calculated and its subsequent radiation zone classification, technically challenging maintenance activities will need to be performed in the Accelerator Systems (AS) and particularly in the HEBT section located inside Target Interface Room (TIR). Thus, all HEBT components inside TIR must be designed to be maintained and replaced by Remote Handling (RH). In order to fulfil the RH requirements, a modularity approach intends to divide HEBT line section into independent large replacement units (LRUs), with localized interfaces. Splitting this section in three modules allows, first, the identification of faulty components more easily and secondly, reduces time needed for their replacement. In addition, the design of these modules has been performed to comply with beam dynamics, vacuum, building, RAMI and positioning requirements established for this section of the HEBT. As a result of the work done, the conclusions concerning the whole design process are summarized at the end of the paper.

Analyzing Uncertain Dynamical Systems after State-Space Transformations into Cooperative Form: Verification of Control and Fault Diagnosis

When modeling real-life applications, uncertainty appears in the form of, for example, modeling approximations, measurement errors, or simply physical restrictions. Those uncertainties can either be treated as stochastic or as bounded, with known limits in the form of intervals. The latter is considered in this paper for a real-life application in the form of an electrical circuit. This is reasonable because the electrical circuit is subject to uncertainties, mainly due to circuit element tolerances and variable load conditions. Since conservative worst-case limits for such parameters are commonly known, interval methods can be applied. The aim of this paper is to demonstrate a possible overall handling of the given uncertain system. Firstly, this includes a control and a reliable computation of the states’ interval enclosures. On the one hand, this can be used to predict the system’s behavior, and on the other hand to verify the control numerically. Here, the implemented feedback control is based on linear matrix inequalities (LMIs) and the states are predicted using an interval enclosure technique based on cooperativity. Since the original system is not cooperative, a transformation is performed. Finally, an observer is implemented as a diagnosis tool regarding faulty measurements or component failures. Since adding a state-of-the-art observer would destroy this structure, a cooperativity-preserving method is applied. Overall, this paper combines methods from robust control design and interval-based evaluations, and presents a suitable observer technique to show the applicability of the presented methods.

Robust fault diagnosis for DC–DC Boost converters via switched systems

In this paper, an effective system for fault detection and fault identification is proposed for DC–DC Boost converters, and the effectiveness of the method is proved from theoretical analysis, simulation, and experimental verification respectively. The switched systems are modeled and some of the fault models are analyzed based on the switched characteristics of the DC–DC Boost converter firstly. Then, the core of the method in this paper is to design a robust linear switch fault detection observer and a set of fault recognition observers. The fault detection observer detects a fault based on the residual among physical output and estimated output. Once the system detects faults, it automatically turns on the identification observers. Meanwhile, the system selects appropriate adaptive rules to estimate the value of the faulty component and achieves the adaptive parameter identification. The location of the fault is identified by comparing several residual evaluation functions. Finally, simulation and semi-physical simulation results testify to the effectiveness of the proposed approach.

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.

Minimal Cardinality Diagnosis in Problems with Multiple Observations.

Model-Based Diagnosis (MBD) is a well-known approach to diagnosis in medical domains. In this approach, the behavior of a system is modeled and used to identify faulty components, i.e., once a symptom of abnormal behavior is observed, an inference algorithm is run on the system model and returns possible explanations. Such explanations are referred to as diagnoses. A diagnosis is an assumption about which set of components are faulty and have caused the abnormal behavior. In this work, we focus on the case where multiple observations are available to the diagnoser, collected at different times, such that some of these observations exhibit symptoms of abnormal behavior. MBD with multiple observations is challenging because some components may fail intermittently, i.e., behave abnormally in one observation and behave normally in another, while other components may fail all the time (non-intermittently). Inspired by recent success in solving classical diagnosis problems using Boolean satisfiability (SAT) solvers, we describe two SAT-based approaches to solve this MBD with multiple observations problem. The first approach compiles the problem to a single SAT formula, and the second approach solves each observation independently and then merges them together. We compare these two approaches experimentally on a standard diagnosis benchmark and analyze their pros and cons.

AADL-Based safety analysis using formal methods applied to aircraft digital systems

Model-based engineering tools are increasingly being used for system-level development of safety-critical systems. Architectural and behavioral models provide important information that can be leveraged to improve the system safety analysis process. Model-based design artifacts produced in early stage development activities can be used to perform system safety analysis, reducing costs, and providing accurate results throughout the system life-cycle. In this paper we describe an extension to the Architecture Analysis and Design Language (AADL) that supports modeling of system behavior under failure conditions. This safety annex enables the independent modeling of component failures and allows safety engineers to weave various types of fault behavior into the nominal system model. The accompanying tool support uses model checking to verify safety properties in the presence of faults and comprehensively enumerate all applicable fault combinations leading to failure conditions under quantitative objectives as part of the safety assessment process. The approach allows exploration of the effects of faulty component behavior on system level failure conditions without requiring explicit propagation specifications. It also supports a shared system model, a modeling language that can describe real-time embedded systems, and usable safety analysis artifacts.

SqSelect: Automatic assessment of Failed Error Propagation in state-based systems

Current software systems are inherently complex and this fact strongly complicates, and makes more expensive, to validate them. Therefore, it is a must to provide methodologies, supported by tools, that can direct validation activities so that they focus on specific aspects of the system (e.g. its critical parts, common errors produced by developers, components that are expensive to fix after deployment, etc). Among the different validation techniques, testing is the most widely used. In this paper we focus on one of the main problems when testing systems with many components: the likelihood of Failed Error Propagation (FEP). FEP appears when we have faulty components such that their wrong behaviour is not revealed when isolatedly testing them but that might produce an error when they are combined with other components. Given a component, it is not possible to automatically assess the likelihood of FEP. However, previous work has shown that there is a strong correlation between the likelihood of FEP and an Information Theory notion called Squeeziness . Recent work has shown that it is possible to compute different values of Squeeziness (essentially, Squeeziness depends on a positive real value) and some of them are more suitable to estimate FEP. In this paper we present our tool SqSelect. Our tool receives either a specific system or its more important characteristics (number of states, maximum and minimum number of outgoing transitions from a state, size of the input and output alphabets) and returns interesting data that can help the tester to estimate the presence of FEP. In particular, our tool provides the most promising value(s) of the parameter associated with Squeeziness so that the likelihood of FEP can be more accurately estimated. In order to compute these values, our tool relies on an artificial neural network that has been extensively trained (compressing the information from around 250,000 systems and around 1,500,000 executions).

A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits.

Anticipatory Troubleshooting

Troubleshooting is the process of diagnosing and repairing a system that is behaving abnormally. It involves performing various diagnostic and repair actions. Performing these actions may incur costs, and traditional troubleshooting algorithms aim to minimize the costs incurred until the system is fixed. Prognosis deals with predicting future failures. We propose to incorporate prognosis and diagnosis techniques to solve troubleshooting problems. This integration enables (1) better fault isolation and (2) more intelligent decision making with respect to the repair actions to employ to minimize troubleshooting costs over time. In particular, we consider an anticipatory troubleshooting challenge in which we aim to minimize the costs incurred to fix the system over time, while reasoning about both current and future failures. Anticipatory troubleshooting raises two main dilemmas: the fix–replace dilemma and the replace-healthy dilemma. The fix–replace dilemma is the question of how to repair a faulty component: fixing it or replacing it with a new one. The replace-healthy dilemma is the question of whether a healthy component should be replaced with a new one in order to prevent it from failing in the future. We propose to solve these dilemmas by modeling them as a Markov decision problem and reasoning about future failures using techniques from the survival analysis literature. The resulting algorithm was evaluated experimentally, showing that the proposed anticipatory troubleshooting algorithms yield lower overall costs compared to troubleshooting algorithms that do not reason about future faults.

Peripheral Diagnosis for Propagated Network Faults

Failures are unavoidable in communication networks, so their detection and identification are vital for the reliable operation of the networks. The existing fault diagnosis techniques are based on many paradigms derived from different areas (e.g., mathematical theories, machine learning, statistical analysis) and with different purposes, such as, obtaining a representation model of the network for fault localization, selecting optimal probe sets for monitoring network devices, reducing fault detection time, and detection of faulty components in the network. Nevertheless, there are still challenges to be faced because those techniques are invasive on account of they increase network traffic and the control overhead. Also, they intensify the internal processes of the network through expanding management processes or monitoring agents on almost all networking devices. This paper introduces a non-invasive fault detection approach based on the observation of symptoms of internal network failures in gateway routers (called peripheral elements). We developed a link failure induction experiment in an emulated network that evidenced the existence of the fault propagation phenomenon to a peripheral level, which demonstrates the feasibility of our approach. Our results foster the use of learning techniques which do not require a complete dependency model of the network and could continuously diagnose the failure symptoms while being resilient to the dynamic changes of the network.

LEVEL OF REPAIR ANALYSIS INCLUDING FAILURE ANALYSIS AND OPTIMAL LOCATION OF MAINTENANCE FACILITIES AND RESOURCES

ABSTRACT Level of repair analysis (LORA) aims to determine the optimal repair policy for complex systems’ components. A repair policy is an a priori decision about which faulty components to discard or repair, and where these actions should take place. Traditionally, LORA models have assumed the maintenance network as pre-defined and identified the resources required to perform the maintenance at each facility as an output. In this paper, the maintenance network itself is an output rather than an input. Other advantages are the ability to deploy different types of resources at the operational level and to allow precise identification of the faulty component. We propose a mixed integer programming (MIP) formulation for the optimization problem, associated with a flow model. Experiments using a set of hypotheticals, but adequate for the purposes of the study, instances provide strong evidence that the formulation’s capabilities can lead to significant cost savings.