Towards standardisation of no fault found taxonomy

There is a phenomenon which exists in complex engineered systems, most notably those which are electrical or electronic which is the inability to diagnose faults reported during operation. This includes difficulties in detecting the same reported symptoms with standard testing, the inability to correctly localise the suspected fault and the failure to diagnose the problem which has resulted in maintenance work. However an inconsistent terminology is used in connection with this phenomenon within both scientific communities and industry. It has become evident that ambiguity, misuse and misunderstanding have directly compounded the issue. The purpose of this paper is to work towards standardisation of the taxonomy surrounding the phenomena popularly termed No Fault Found, Retest Okay, Cannot Duplicate or Fault Not Found amongst many others. This includes discussion on how consistent terminology is essential to the experts within organisation committees and, to the larger group of users, who do not have specialised knowledge of the field

Impact of incomplete ventricular coverage on diagnostic performance of myocardial perfusion imaging.

In the context of myocardial perfusion imaging (MPI) with cardiac magnetic resonance (CMR), there is ongoing debate on the merits of using technically complex acquisition methods to achieve whole-heart spatial coverage, rather than conventional 3-slice acquisition. An adequately powered comparative study is difficult to achieve given the requirement for two separate stress CMR studies in each patient. The aim of this work is to draw relevant conclusions from SPECT MPI by comparing whole-heart versus simulated 3-slice coverage in a large existing dataset. SPECT data from 651 patients with suspected coronary artery disease who underwent invasive angiography were analyzed. A computational approach was designed to model 3-slice MPI by retrospective subsampling of whole- heart data. For both whole-heart and 3-slice approaches, the diagnostic performance and the stress total perfusion deficit (TPD) score-a measure of ischemia extent/severity-were quantified and compared. Diagnostic accuracy for the 3-slice and whole-heart approaches were similar (area under the curve: 0.843 vs. 0.855, respectively; P = 0.07). The majority (54%) of cases missed by 3-slice imaging had primarily apical ischemia. Whole-heart and 3-slice TPD scores were strongly correlated (R2 = 0.93, P < 0.001) but 3-slice TPD showed a small yet significant bias compared to whole-heart TPD (- 1.19%; P < 0.0001) and the 95% limits of agreement were relatively wide (- 6.65% to 4.27%). Incomplete ventricular coverage typically acquired in 3-slice CMR MPI does not significantly affect the diagnostic accuracy. However, 3-slice MPI may fail to detect severe apical ischemia and underestimate the extent/severity of perfusion defects. Our results suggest that caution is required when comparing the ischemic burden between 3-slice and whole-heart datasets, and corroborate the need to establish prognostic thresholds specific to each approach

Development and validation of a new glaucoma screening test using temporally modulated flicker

Purpose Describing the psychometric characteristics and diagnostic accuracy of the Accelerator 4‐Alternative Forced‐Choice Flicker Test prototype (A4FTp) for detecting chronic open angle glaucoma (COAG). Methods A4FTp measures temporally‐modulated flicker thresholds in regions of the visual field with high susceptibility to glaucomatous loss. We initially evaluated its psychometric properties on 20 normals (aged 33.8 ± 8.5 years) who were tested multiple times over a period of 3 months. All subjects underwent four repetitions for shorter (T8) and longer (T12) staircase termination criteria, to determine the most suitable threshold criterion. Four randomly selected subjects underwent a total of 10 repetitions to study test‐retest repeatability and learning effects. To determine its diagnostic accuracy, one eye of 40 participants with COAG and 38 normal controls were tested with the A4FTp in comparison with the Frequency Doubling Technology (FDT; C20‐5 programme) and iVue Spectral Domain Optical Coherence Tomography (SD‐OCT). Tests were conducted in a random order with results masked to the clinician conducting the reference ophthalmic examination. The accuracy of each test was determined by analysis of the area under the receiver operator characteristic curve (AUROC). Results A4FTp flicker thresholds were stable, with standard deviations of only 0.52 decilog (dL) for T8, increasing to 1.32 dL for T12, and no significant flicker sensitivity threshold improvement over the 10 repeat runs. T8 was superior to T12 on several other measures, so it was used for the remaining comparisons. In terms of diagnostic accuracy, the mean AUROC for the three tests were A4FTp [T8 criterion; 0.82, 95% confidence interval (0.73–0.92)]; SD‐OCT [any RNFL parameter p < 1% level; 0.90 (0.83–0.97)]; and FDT [one or more locations missed at p < 5% level; 0.91 (0.82–0.96)]. There was no statistical difference in AUROC between A4FTp and SD‐OCT (p = 0.18) or FDT (p = 0.12). The A4FTp test duration averaged just over 2 min per eye, taking approximately one‐third of the time for completion of the HFA SITA 24‐2 algorithm (conducted as part of the reference examination) and twice the time for the suprathreshold FDT. Conclusion Test accuracy for the A4FTp was comparable to those of the FDT and SD‐OCT for the detection of COAG. Time taken to complete the A4FTp was relatively short and initial results are promising. With further refinement, the A4FTp could have a future role in glaucoma detection

A Lightweight N-Cover Algorithm For Diagnostic Fail Data Minimization

The increasing design complexity of modern ICs has made it extremely difficult and expensive to test them comprehensively. As the transistor count and density of circuits increase, a large volume of fail data is collected by the tester for a single failing IC. The diagnosis procedure analyzes this fail data to give valuable information about the possible defects that may have caused the circuit to fail. However, without any feedback from the diagnosis procedure, the tester may often collect fail data which is potentially not useful for identifying the defects in the failing circuit. This not only consumes tester memory but also increases tester data logging time and diagnosis run time. In this work, we present an algorithm to minimize the amount of fail data used for high quality diagnosis of the failing ICs. The developed algorithm analyzes outputs at which the tests failed and determines which failing tests can be eliminated from the fail data without compromising diagnosis accuracy. The proposed algorithm is used as a preprocessing step between the tester data logs and the diagnosis procedure. The performance of the algorithm was evaluated using fail data from industry manufactured ICs. Experiments demonstrate that on average, 43% of fail data was eliminated by our algorithm while maintaining an average diagnosis accuracy of 93%. With this reduction in fail data, the diagnosis speed was also increased by 46%

Integrating Local Binary Pattern Image Transformations and Customized Deep Learning Models for Enhanced Fetal Cardiac Anomaly Detection

This research focuses on developing a deep learning diagnostic model for diagnosing fetal cardiac anomalies from real time ultrasound scan images. The dataset framed in the previous research is transformed using Local Binary Pattern (LBP) technique which is trained with the deep learning models to create the classifiers. These models are used to classify the Real Time images captured by ultrasound scanning machines. The LBP is a texture image feature. LBP captures the local structure and patterns within an image by comparing the intensity values of each pixel with its surrounding neighbours. The LBP operator assigns a binary code to each pixel based on the comparison results, resulting in a texture representation of the image. The FetaEcho_V05 dataset is transformed into an LBP image dataset titled as FetalEcho_V0501. This dataset is used for creating the classifiers by training the custom CNN(CCNN), AlexNet, VGG16 and ResNet50 deep learning models. The classifiers are evaluated for its overall classification performance and class wise evaluation performance using the metrics the precision, recall, accuracy and F1 score metrics. When the overall performance is considered, the CCCNN model performed the best on the FetalEcho_V0501 dataset

진동 생성 메커니즘을 고려한 초기 결함 단계의 베어링 진단 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 윤병동.The failure of rolling element bearings is a common fault in rotating machines. These failures can have catastrophic consequences, including fatal injuries and significant financial losses. To mitigate these risks, researchers have explored various ways to detect and prevent bearing failures as early as possible. One promising approach is the use of condition monitoring data; in this approach, vibration data has been found to be particularly effective for identifying and preventing potential failures. However, the use of vibration signals to diagnose bearings at the incipient fault stage is a challenging task, in part due to the gap between the controlled conditions under which research data is often generated and the actual field conditions in which these bearings operate. In particular, fault-related signals are weak and nonstationary; further, they are usually obscured by noise that arises from environmental factors. Additionally, these signals may be complicated or modulated, making them difficult to discern. To properly address these research issues, this dissertation aims at advancing two research thrusts focused on developing techniques for modeling and analyzing vibration signals based on physical phenomena. In Research Thrust 1, a quasi-periodic impulse train model with an impact force function is suggested to brtidge the gap between theory and reality. In this research, a pseudo second-order cyclostationary signal is modeled using the quasi-periodic impulse train model. In order to simulate the dynamic response of a system, considering the physical behaviors in bearings, the impact force function that reflects the change in contact stress is used. Finally, the proposed model is validated by performing signal processing on the synthesized signal, including simulation of the proposed model. The result confirm that an appropriate preprocessing process is essential to diagnose bearing failure at the incipient failure stage, further, that finding the frequency band that contains the failure information is essential for performance improvement. In Research Thrust 2, a new feature extraction method is proposed for bearing diagnosis using vibration signals, namely the linear power normalized cepstral coefficients (LPNCC). The proposed approach is designed to enhance the bearing signal, which is buried in noise that arises from environmental effects, and which contains mechanical phenomena. The proposed method consists of two sequentially executed steps: 1) extraction of the LPNCC and 2) demodulation analysis that is performed by examining the squared envelope spectra (SES). Combined, this approach is called LPNCC-SES. The performance of the proposed method is examined by applying it to both simulation data and experimental cases. The results show a high level of accuracy and robustness in the diagnostic capabilities of the method, making it suitable for use in maintenance and diagnostic routines.구름 베어링은 회전 기계 및 왕복동 기계의 핵심적인 요소부품으로 회전하거나 진동하는 구조를 지지하며 구성품 간의 하중을 전달한다. 따라서 구름 베어링의 고장은 시스템 전체의 고장으로 이어져 치명적인 인명 피해는 물론 막대한 재정적 손실을 초래할 수 있다. 이에 따라 조기에 베어링의 고장을 관측하고 진단하기 위해 상태관측 데이터를 활용한 많은 연구가 진행되어 왔으며 특히 진동신호를 활용한 진단이 널리 수행되었다. 베어링 초기 결함을 진단을 하는데 있어 어려움을 겪게 하는 이유로 환경 영향으로 인해 발생하는 잡음에 묻혀 있는 약한 결함 신호 및 베어링의 결함 관련 신호의 복잡한 변조를 들 수있다. 이러한 문제를 극복하기 위해 본 연구에서는 베어링 결함신호의 생성원리에에 기반한 신호 모델을 제안하였다. 베어링 신호는 본질적으로 비정상성을 띄며 또한 실제 현장에서 획득한 신호는 복잡하고 다양한 소스에서 발생하는 신호가 조합된다. 이론과 현실 사이의 격차를 해소하기 위해 해석적 신호 모델에 헤르츠 접촉 이론에 기반한 충격 메커니즘을 구현하였다. 시뮬레이션된 베어링 신호에 기어의 결정론적 신호, 회전축의 사인파 신호 및 가우시안 노이즈와 합성된 신호에 대한 전처리 분석을 통해 제안 모델의 타당성을 검증하였다. 이 후, 다양한 잡음 환경에서 여러 변조된 음성 신호를 효과적으로 판별하는 음성인식 방법을 기계시스템에 적용한 고장특징 추출 방법을 새로이 제안하여 캡스트럽에 기반한 특징인자를 추출하였다. 추출된 인자로부터 시간-주파수 영역에서 스펙트럼을 계산하여 효과적으로 베어링의 특성 주파수를 검출하였다. 제안된 방법의 검증을 위해 다양한 잡음 환경에서의 시뮬레이션 데이터와 실험데이터를 사용하였다. 또한 가속수명시험을 통한 데이터를 통하여 조기진단의 효과를 검증하였다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 5 Chapter 2. Technical Background and Literature Review 6 2.1 Vibration Signals of Bearing Faults 6 2.1.1 Rolling Element Bearings 6 2.1.2 Failure of Rolling Element Bearings 7 2.1.3 Bearing Fault Signature and Its Frequencies 8 2.2 Vibration Techniques for Bearing Incipient Fault Diagnosis 10 2.2.1 Overview of Vibration Techniques for Bearings 10 2.2.2 Cepstrum-Based Fault Diagnosis Techniques 13 Chapter 3. Quasi Periodic Impulse Train Model with Impact Force Function 20 3.1 Vibration Modelling of Bearing Fault 21 3.1.1 General Mathematical Model 21 3.1.2 Quasi-periodic Model with Cyclostationary 22 3.1.3 Excitation Force Function in Dynamic Models 23 3.2 Quasi Period Impulse Model with Impact Function 26 3.2.1 Overall Process of Proposed Model 26 3.2.2 Modeling the Excitation Force 27 3.3 Numerical Results and Discussion 32 3.3.1 Necessity of Choosing an Appropriate Preprocessing Method 34 Chapter 4. Speech Recognition-Inspired Feature Engineering for Bearing Fault Diagnosis 48 4.1 Review of Power-Normalized Cepstral Coefficients (PNCC) 49 4.1.1 Basic Definition of Cepstrum 49 4.1.2 Characteristics of cepstrum in mechanical vibrations 50 4.1.3 Power-Normalized Cepstral Coefficients (PNCC) 52 4.2 Proposed Feature Extraction Method: Linear Power-Normalized Cepstral Coefficients (LPNCC) 55 4.3 Fault Diagnosis by Implementing LPNCC 57 4.3.1 Fault Diagnosis Method using LPNCC and Squared Envelope Spectrum (LPNCC-SES) 57 4.3.2 Effect of Linear Filter and Power-normalization 59 4.4 Experimental Application and Results 60 4.4.1 Case Study with Simulation Model 61 4.4.1.1. Simulation Data with White Gaussian Noise 61 4.4.1.2. Denoising Under Gaussian Noise 62 4.4.1.3. Reseults Under Non-gaussian Noise 66 4.4.2 Case Study with Experiment Data 67 4.4.2.1. Experimental Data: Case Western Reserve University Dataset 67 4.4.2.1.1. Compared Methods 67 4.4.2.1.2. Case 1: Impusive Noise 68 4.4.2.1.3. Case 2: Low Signal-to-noise Ratio (SNR) 69 4.4.2.1.4. Case 3: Multiple Defective Signals 71 4.4.2.2. Experimental Data: Naturally Degradation Data 72 Chapter 5. Conclusions 108 5.1 Summary of Dissertation 108 5.2 Contributions and Significance 110 5.3 Suggestions for Future Research 113 References 116 Abstract (Korean) 130박

Diagnosis of Primary Ciliary Dyskinesia

Primary ciliary dyskinesia (PCD) is a rare genetic disease leading to bronchiectasis in most patients. In addition to the lungs, PCD might affect multiple organ systems, and patients frequently have multiple clinical problems, which require multidisciplinary management. Diagnosis of PCD needs a combination of tests, many of which require expertise and expensive equipment. Measurement of nasal nitric oxide is the first test to consider when PCD is suspected. Detailed clinical history using available predictive scores in combination with information on functional and structural aspects of lung disease is important to identify which patients should be referred for further diagnostic testing

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

Abstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings