A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis

Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.

Study of Ontology-Based Swine Diagnosis Technology

The computer swine disease diagnosis is an important tool for swine farming industry, but the traditional expert system cannot meet the requirement of practical application. To improve the situation, a swine disease ontology is constructed, which can model the knowledge of swine disease diagnosis into a concept system, and a mechanism that can save the ontology into relational database is established, further more a computer system is developed to implement ontology- based swine disease diagnosis, so make the diagnosis results extended and more precise.

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear backpropagation based contribution(LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder(AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution(RBC), the propagation of fault information is described by using back-propagation(BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well,and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.

Irritable bowel syndrome: Pathogenesis, diagnosis, treatment, and evidence-based medicine

Irritable bowel syndrome(IBS)is a chronic and debilitating functional gastrointestinal disorder that affects9%-23%of the population across the world.The percentage of patients seeking health care related to IBS approaches 12%in primary care practices and is by far the largest subgroup seen in gastroenterology clinics.It has been well documented that these patients exhibit a poorer quality of life and utilize the health care system to a greater degree than patients without this diagnosis.The pathophysiology of IBS is not clear.Many theories have been put forward,but the exact cause of IBS is still uncertain.According to the updated ROMEⅢcriteria,IBS is a clinical diagnosis and presents as one of the three predominant subtypes:(1)IBS with constipation(IBS-C);(2)IBS with diarrhea(IBS-D);and(3)mixed IBS(IBS-M);former ROME definitions refer to IBS-M as alternating IBS(IBS-A).Across the IBS subtypes,the presentation of symptoms may vary among patients and change over time.Patients report the most distressing symptoms to be abdominal pain,straining,myalgias,urgency,bloating and feelings of serious illness.The complexity and diversity of IBS presentation makes treatment difficult.Although there are reviews and guidelines for treating IBS,they focus on the efficacy of medications for IBS symptoms usinghigh-priority endpoints,leaving those of lower priority largely unreported.Therefore,the aim of this review is to provide a comprehensive evidence-based review of the diagnosis,pathogenesis and treatment to guide clinicians diagnosing and treating their patients.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.

Machinery fault diagnosis expert system based on case-based reasoning

A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

Automated Pulse-Based Diagnosis: Role of TIM Diagnostic Features

Emanated from the idea of reinvestigating ancient medical system of Ayurveda—Traditional Indian Medicine (TIM), our recent study had shown significant applications of analysis of arterial pulse waveforms for non-invasive diagnosis of cardiovascular functions. Here we present results of further investigations analyzing the relation of pulse-characteristics with some clinical and pathological parameters and other features that are of diagnostic importance in Ayurveda.

Dual-priming oligonucleotide-based multiplex PCR using tissue samples in rapid urease test in the detection of Helicobacter pylori infection

AIM:To investigate whether tissue samples processed by the rapid urease test(RUT)kit are suitable for dualpriming oligonucleotide-based multiplex polymerase chain reaction(DPO-PCR)to detect Helicobacter pylori(H.pylori).METHODS:A total of 54 patients with specific gastrointestinal symptom were enrolled in this study.During endoscopy,gastric biopsy specimens were taken for histology,RUT,and DPO-PCR.DPO-PCR was performed on gastric biopsy samples and tissue samples that were analyzed by RUT at 2 separate institutes.In detecting H.pylori,the concordance rate of the DPO-PCR tests between the tissue samples that had been submitted to RUT and the gastric biopsy samples was investigated.RESULTS:H.pylori co-occurred with 76.0%(19/25)of gastric ulcers,64.3%(9/14)of duodenal ulcers,and 33.3%(4/12)of gastritis cases.H.pylori infection was found in 100%(3/3)of the patients with both gastric and duodenal ulcers.Overall,H.pylori was detected in 35 of 54(64.8%)patients.The diagnostic sensitivities of histology,RUT,and DPO-PCR were85.7%(30/35),74.3%(26/35),and 97.1%(34/35),respectively(P=0.02).The positive predictive value(PPV)of DPO-PCR was 94.4%,whereas the negative predictive value(NPV)was 94.7%.In the rapid urease test(CLOtest)-negative cases,the frequency of positive DPO-PCR and histologic results was 20.0%(7/35).The concordance rate of the DPO-PCR tests between the tissue samples from the RUT kit and the gastric biopsy samples was 94.4%(51/54).The rate of DPOPCR and silver stain positivity in the RUT-negative cases was 20.0%(7/35).CONCLUSION:In diagnosing H.pylori infection,DPO-PCR can be performed on tissue samples that have been processed by the RUT kit.Particularly,in patients with RUT-negative results,DPO-PCR on these tissue samples could be helpful in detecting of H.pylori infection.

A NEW DIAGNOSIS APPROACH BY THE STRATIFIED ATMS

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Study on Fault Diagnosis of Rotating Machinery with Hybrid Neural Networks

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A new interpretable fault diagnosis method based on belief rule base and probability table

It is vital to establish an interpretable fault diagnosis model for critical equipment.Belief Rule Base(BRB)is an interpretable expert system gradually applied in fault diagnosis.However,the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features.In addition,the interpretability of BRB-based fault diagnosis is destroyed in the optimization process,which reflects in two aspects:deviation from the initial expert judgment and over-optimization of parameters.To solve these problems,a new interpretable fault diagnosis model based on BRB and probability table,called the BRB-P,is proposed in this paper.Compared with the traditional BRB,the BRB-P constructed by the probability table is more accurate.Then,the interpretability constraints,i.e.,the credibility of expert knowledge,the penalty factor and the rule-activation factor,are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P.A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.

Sensor Fault Diagnosis and Tolerant Control Based on Belief Rule Base for Complex System

This paper develops a new fault diagnosis and tolerant control framework of sensor failure(SFDTC)for complex system such as rockets and missiles.The new framework aims to solve two problems:The lack of data and the multiple uncertainty of knowledge.In the SFDTC framework,two parts exist:The fault diagnosis model and the output reconstruction model.These two parts of the new framework are constructed based on the new developed belief rule base with power set(BRB-PS).The multiple uncertainty of knowledge can be addressed by the local ignorance and global ignorance in the new developed BRB-PS model.Then,the stability of the developed framework is proved by the output error of the BRB-PS model.For complex system,the sensor state is determined by many factors and experts cannot provide accurate knowledge.The multiple uncertain knowledge will reduce the performance of the initial SDFTC framework.Therefore,in the SFDTC framework,to handle the influence of the uncertainty of expert knowledge and improve the framework performance,a new optimization model with two optimization goals is developed to ensure the smallest output uncertainty and the highest accuracy simultaneously.A case study is conducted to illustrate the effectiveness of the developed framework.

Inquiry Based on Evidence and its Practices in Two Healthcare Facilities of Bujumbura

Background: Inquiry evidence-based practice (IBP) improves healthcare quality, reliability, and patient outcomes as well as reduces variations in care and costs. IBP and its practice in health care promote also many advantages, such as improvements of practices based on the attitudes and cognitive ideas. This study aims to assess the inquiry based on evidence (IBP) and its practices in two Health Care Facilities (HCFs) of Bujumbura to help the practitioners to understand its importance. Methods: A cross-sectional study design was used to analyze the importance of IBP and its practice in these two hospitals. The probability-sampling technique was used also to select 104 nurses from the Military Hospital of Kamenge and 55 nurses from the Van Norman Clinic. A questionnaire was used to collect data with two mains components, demographic data and knowledge and attitudes addressing the following parameters: evidence practice during inquiry, nursing theory, current analysis in nursing care oriented the evidence, prioritization of care, rational diagnostic, monitoring and assessment. Results: The findings from this study revealed a poor knowledge and attitude among participants towards Inquiry Based Practice. In all variables, participants were scoring less than 10%. However, majority of participants (76.5%) know the indicators of patients’ satisfaction with nursing interventions through survey-based practice and 74.1% argued to analyze their information collected. Conclusion: This study revealed a weak awareness on IBP and its importance during nursing practice among participants as for almost all variables, participants were scoring less than 10%, except for the indicators of patients’ satisfaction with nursing interventions through survey-based practice (76.5%). Therefore, in-service training and curriculum revision had been highlighted and recommended another to provide the best rational diagnosis and achieve the patient’s outcomes.

Variable selection-based SPC procedures for high-dimensional multistage processes

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.