An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further

ANFIS: Establishing and Applying to Managing Online Damage

Fuzzy logic (FL) and artificial neural networks (ANNs) own individual advantages and disadvantages. Adaptive neuro-fuzzy inference system (ANFIS), a fuzzy system deployed on the structure of ANN, by which FL and ANN can interact to not only overcome their limitations but also promote the ability of each model has been considered as a reasonable option in the real fields. With the vital strong points, ANFIS has been employed well in many technology applications related to filtering, identifying, predicting, and controlling noise. This chapter, however, focuses mainly on building ANFIS and its application to identifying the online bearing fault. First, a traditional structure of ANFIS as a data-driven model is shown. Then, a recurrent mechanism depicting the relation between the processes of filtering impulse noise (IN) and establishing ANFIS from a noisy measuring database is presented. Finally, one of the typical applications of ANFIS related to online managing bearing fault is shown

A review on artificial intelligence in high-speed rail

High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided

Intelligent fault detection and classification based on hybrid deep learning methods for Hardware-in-the-Loop test of automotive software systems

Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

Characterisation of the Dynamics of an Automotive Suspension System for On-line Condition Monitoring

As the most critical system that determines the driving performance, passenger comfort and road safety of a vehicle, the suspension system has not been found to have adequate monitoring systems available to provide early warnings of possible faults online. To fill this gap, this study has focused on the investigation of the dynamic behaviour of the suspensions upon which a new on-line condition suspension monitoring approach was proposed and verified under different conditions. Specifically, the approach quantifies the modal shapes which are obtained based on an improved modal identification applying to acceleration responses at the four corners of the vehicle. To achieve this, the research was carried out by the means of dynamic modelling, numerical simulations, optimal measurement optimisations and subspace identification improvements based on a representative vehicle system, cost-effective measurement techniques and road standards. Firstly, a mathematical model with a seven degree-of-freedom (7-DOF) was developed in account of variable stiffness and damping coefficients, being applicable for computer simulation of the dynamic interaction between a vehicle and a road profile. To validate the proposed model during real operation, this study investigates a set of on-road experiments, to measure the acceleration of the vehicle body. Comparisons between the experimental and simulation paths demonstrated that, simulation results and measured on road results were found to be almost have similar trend. In the simulations the modal parameters (obtained theoretically) of a vehicle are: natural frequency, damping ratio and modal shapes and their characteristics are characterised under the influence of different suspension faults and operating conditions (loads and speed). It has found that the modal shapes are more independent of operating conditions and thereby reliable as indicators of faulty suspensions, compared with modal frequency and damping which are influenced more by operating conditions. Furthermore, the modal shape difference between left and right side responses are developed as the fault severity indicator. To obtain the modal shapes online reliably, an improved stochastic subspace identification (SSI) is developed based on an average correlation SSI. Particularly the implementation of optimal reference channels is achieved by comparing the average correlation signals which can be more efficient due to much smaller data sizes, compared with that raw data based spectrum analysis method used in original development. On road verification based on a commercial vehicle operating in normal road conditions shows that common suspension faults including inadequate damping faults and under-inflation of the tyre, induced one of the four shock absorbers, can be detected and diagnosed with acceptable accuracy. Therefore, it can be deduced that the SSI modal shape based detection techniques are effective and therefore promising to be used to diagnose and monitor the suspension system online

Machine Learning Use-Cases in C-ITS Applications

In recent years, the development of Cooperative Intelligent Transportation Systems (C-ITS) have witnessed significant growth thus improving the smart transportation concept. The ground of the new C-ITS applications are machine learning algorithms. The goal of this paper is to give a structured and comprehensive overview of machine learning use-cases in the field of C-ITS. It reviews recent novel studies and solutions on CITS applications that are based on machine learning algorithms. These works are organised based on their operational area, including self-inspection level, inter-vehicle level and infrastructure level. The primary objective of this paper is to demonstrate the potential of artificial intelligence in enhancing C-ITS applications

Investigation on Statistical Model Calibration and Updating of Physics and Data-driven Modeling for Hybrid Digital Twin

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2022.2. 윤병동.실제 운행중인 공학 시스템의 가상 디지털 객체를 구축하여 시스템의 관측 데이터를 기반으로 실제 시스템의 다양한 상황을 모사할 수 있는 디지털 트윈 기술은 설계, 제조 및 시스템 상태 관리와 같은 공학적 의사 결정을 지원할 수 있는 솔루션을 제공합니다. 디지털 트윈 접근 방식은 1) 데이터 기반 접근 방식, 2) 물리 기반 접근 방식, 3) 융합형 접근 방식의 세 가지 범주로 나눌 수 있습니다. 융합형 디지털 트윈은 데이터 기반 모델과 물리 기반 모델을 모두 활용하여 이 두 가지 접근 방식의 단점을 극복하기 때문에 관찰된 데이터를 바탕으로 신뢰할 수 있는 공학적 의사 결정을 가능하게 합니다. 그러나 이를 적용하기 위해 필요한 시스템에 대한 사전 정보들은 대부분의 공학 시스템에서 제한적으로 이용 가능합니다. 이러한 사전 정보에는 모델 입력 변수의 통계적 정보, 데이터 기반 혹은 물리 기반 모델링에 필요한 모델링 정보, 시스템 이상 상태에 대한 물리적 사전 지식이 포함됩니다. 많은 경우, 주어진 사전 정보가 충분하지 않은 상황에서 디지털 트윈을 활용한 의사 결정의 신뢰성 문제가 발생합니다. 통계적 모델 보정 및 갱신 방법은 불충분한 사전 정보 하에서 디지털 트윈 분석을 검증 및 고도화하는 데 사용할 수 있습니다. 본 박사 학위 논문은 사전 정보가 부족한 상황에서 융합형 디지털 트윈을 구축하기 위해 모델 보정 및 갱신에서 세 가지 필수 및 관련 연구 분야를 발전시키는 것을 목표로 합니다. 유효한 디지털 트윈 모델을 구축하기 위해서는 다양한 운행 조건에서 충분한 관측 데이터와 시스템 형상, 재료 속성, 작동 조건과 같은 사전 지식이 필요합니다. 그러나 복잡한 엔지니어링 시스템에서는 모델 구축을 위한 사전 정보를 얻기가 어렵습니다. 첫번째 연구에서는 모델 구축에 필요한 사전 지식 부족 시에도 활용 가능한 데이터 기반 동적 모델 갱신 방법을 제안합니다. 제안된 신호 전 처리를 사용하여 관측된 신호에서 시스템 이상 감지를 위한 시간-주파수 영역 특성을 추출합니다. 다양한 작동 조건에서의 시스템 구동 상태를 예측하기 위해 부분 공간 상태 공간 시스템 식별 방법을 이용하여 상태 공간 모델을 구축합니다. 시스템 작동 조건은 시스템 모델의 매개변수화된 입력 신호로 정의됩니다. 다음으로, 신호 관측 시점에서의 시스템 작동 조건과 이상 상태를 추정하기 위해 입력 신호 매개변수는 기준 신호와 관측 신호의 오차를 최소화하도록 갱신됩니다. 모델 입력 변수의 통계적 정보 부족할 경우 최적화 기반 통계 모델 보정을 통해 미지 입력 변수를 추정하여 모델의 예측 능력을 향상시킬 수 있습니다. 최적화 기반 통계 모델 보정은 가상 모델의 예측 응답과 실제 시스템의 관측 응답 간의 통계적 유사성을 최대화하여 모델에 존재하는 미지 입력 변수의 통계적 모수를 추정하는 최적화 문제로 공식화 됩니다. 이때 보정 척도는 통계적 유사성을 정량화하는 목적 함수로 정의됩니다. 두 번째 연구에서는 모델 보정의 정확도와 효율성을 높이기 위해 통계적 상관관계를 고려한 새로운 보정 메트릭인 Marginal Probability and Correlation Residual (MPCR)을 제안합니다. MPCR의 기본 아이디어는 출력 응답 간의 통계적 상관 관계를 고려하면서 다 변량 결합 확률 분포를 수치적 계산 비용이 낮은 다중 주변 확률 분포로 분해하는 것입니다. 디지털 트윈을 이용하여 고장 상태에 대한 사전 지식 부재한 공학 시스템의 고장 상태를 예측하기 위해, 제조 및 실험 조건의 불확실성들이 고려되어야 합니다. 세 번째 연구 방향은 고장 상태에 대한 사전 지식이 부재한 시스템의 피로 균열 시작 및 성장을 추정하기 위한 융합형 디지털 트윈 접근 방식을 제안하였습니다. 본 연구에서는 피로 균열의 시작과 성장을 추정하기 위해 두 가지 기술: (i) 통계적 모델 보정과 (ii) 확률적 요소 갱신을 제안합니다. 통계 모델 보정에서는 균열 시작 조건과 관련된 관찰된 응답을 기반으로 제조 및 실험 조건의 불확실성을 나타내는 입력 변수의 통계적 매개변수를 추정합니다. 통계적 보정을 통해 불확실성을 고려한 확률론적 물리 기반 해석을 통해 균열 시작 및 성장을 나타내는 주요 취약 요소를 예측할 수 있습니다. 확률적 요소 갱신에서는 시스템의 피로 균열 시작 및 성장을 추정하기 위해 균열 성장 조건에서 관찰된 응답을 이용한 최대 우도 법을 갱신 기준으로 모델을 갱신합니다.Digital Twin technology, a virtual representation of the physical entity, has been explored toward providing a solution that could support engineering decisions, such as design, manufacturing, and system health management. Digital twin approaches can be divided into three categories: 1) data-driven, 2) physics-based, and 3) hybrid approaches. The hybrid digital twin is recognized as a promising solution for reliable engineering decisions based on the observed data because it utilizes both the data-driven and physics-based models to overcome the disadvantages of these two approaches. However, the applicability of the digital twin approach has been limited by a lack of prior information. The prior information includes the statistics of model input parameters, required information for (data-driven, physics-based, and hybrid) modeling, and prior knowledge about system failure. Now, a question of fundamental importance arises how to help decision-making using a digital twin under a given insufficient prior information. Statistical model calibration and updating can be used to validate the digital twin analysis under insufficient prior information. In order to build a hybrid digital twin under insufficient prior information, this doctoral dissertation aims the investigation on three co-related research areas in model calibration and updating: Research Thrust 1 – Data-driven dynamic model updating for anomaly detection with an insufficient prior information Research Thrust 2 – A new calibration metric formulation considering the statistical correlation Research Thrust 3 – Hybrid model calibration and updating considering system failure A sufficient prior knowledge such as observed data in various conditions, geometry, material properties, and operating conditions for data-driven / physics-based modeling are required to build a valid digital twin model. However, the prior information for modeling is hard to obtain for complex engineering system. Research Thrust 1 proposes Data-driven dynamic model updating for anomaly detection with insufficient prior knowledge. The time-frequency domain features are extracted from the observed signal using signal pre-processing. The state-space model is driven by a numerical algorithm for subspace state-space system identification (N4SID) to predict the extracted features under different operating conditions. In the model, the operating condition is defined as a parameterized input signal of a system model. Next, the input signal parameters are updated to minimize the prediction error that quantify the discrepancy between the target observed signal and reference model prediction. Optimization-based statistical model calibration (OBSMC) can be applied to estimate unknown input parameters of the digital twin. In OBSMC, the unknown statistical parameters of input variables associated with a digital twin model are inferred by maximizing the statistical similarity between predicted and observed output responses. A calibration metric is defined as the objective function to be maximized that quantifies statistical similarity. Research Thrust 2 proposes a new calibration metric: Marginal Probability and Correlation Residual (MPCR), to improve the accuracy and efficiency of model calibration considering statistical correlation. The foundational idea of the MPCR is to decompose a multivariate joint probability distribution into multiple marginal probability distributions while considering the statistical correlation between output responses. In order to diagnose and predict the system failure of a complex engineering system without prior knowledge about system failure using the digital twin, uncertainties in manufacturing and test conditions must be taken into account. Research Thrust 3 proposed a hybrid digital twin approach for estimating fatigue crack initiation and growth considering those uncertainties. The proposed approach for estimating fatigue crack initiation and growth is based on two techniques; (i) statistical model calibration and (ii) probabilistic element updating. In statistical model calibration, statistical parameters of input variables that indicate uncertainties in manufacturing and test conditions are estimated based on the observed response related to the crack initiation condition. Further, probabilistic analysis using estimated statistical parameters can predict possible critical elements that indicate crack initiation and growth. In probabilistic element updating procedures, the possible crack initiation and growth element is updated based on the Bayesian criteria using observed responses related to the crack growth condition.Abstract i List of Tables ix List of Figures xi Nomenclatures xvi Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 4 1.3 Dissertation Layout 7 Chapter 2 Literature Review 9 2.1 Digital Twin Formulation 9 2.1.1 Data-driven Digital Twin 10 2.1.2 Physics-based Digital Twin 13 2.1.3 Hybrid Digital Twin 17 2.2 Digital Twin Calibration & Updating 18 2.2.1 Optimization-based Statistical Model Calibration 19 2.2.2 Parameter Estimation using Kalman/ Particle filter 24 2.2.3 Summary and Discussion 27 Chapter 3 Data-driven Dynamic Model Updating for Anomaly Detection with an Insufficient Prior Information 28 3.1 System Description of On-Load Tap Changer 30 3.2 Data-driven Dynamic Model Updating for Anomaly Detection with an Insufficient Prior Information 34 3.2.1 Preprocessing of Vibration Signal 37 3.2.2 Reference Model Formulation using N4SID 39 3.2.3 Optimization-based Parameter Updating 43 3.3 Case Study 45 3.3.1 Case Study 1: (Numerical) Vibration Analysis using Parameter Varying Cantilever Beam and Multi-DOF model 45 3.3.2 Case Study 2: Vibration Signal of On Load Tap Changer in Power Transformer 54 3.4 Summary and Discussion 59 Chapter 4 A New Calibration Metric that Considers Statistical Correlation : Marginal Probability and Correlation Residuals 61 4.1 Statistical correlation issue in calibration metric formulation 63 4.1.1 What happens if the statistical correlation is neglected in model calibration? 63 4.1.2 Comments on existing calibration metrics in terms of the statistical correlation 66 4.2 Proposed Method: Marginal probability and correlation residuals (MPCR) 69 4.3 Case Studies 73 4.3.1 Mathematical example 1: Bivariate output responses (Statistical correlation issue 73 4.3.2 Mathematical example 2: Multivariate output responses (Curse of dimensionality issue) 78 4.3.3 Engineering example 1: Modal analysis of a beam structure with uncertain rotational stiffness boundary conditions (Scale issue) 87 4.3.4 Engineering example 2: Crashworthiness of vehicle side impact (High dimensional & nonlinear problem) 93 4.4 Summary and Discussion 101 Chapter 5 Hybrid Model Calibration and Updating for Estimating System Failure 103 5.1 Brief Review of Digital Twin Approaches for Estimating Crack Initiation & Growth 105 5.2 Proposed Digital Twin Approach : Hybrid Model Calibration & Updating 109 5.2.1 Statistical Model Calibration using a Data-driven Twin 110 5.2.2 Probabilistic Element Updating with a Physics-based Twin 114 5.3 Case Study: Automotive Sub-Frame Structure 118 5.3.1 Experimental Fatigue Test 118 5.3.2 Statistical Model Calibration using a Data-driven Twin 121 5.3.3 Element Updating with a Physics-based Twin 127 5.4 Summary and Discussion 131 Chapter 6 Conclusions 133 6.1 Contributions and Significance 133 6.2 Suggestions for Future Research 135 References 138 국문 초록 155박

Observer-based Anomaly Diagnosis and Mitigation for Cyber-Physical Systems

Cyber-Physical Systems (CPS) seamlessly integrate computational devices, communication networks, and physical processes. The performance and functionality of many critical infrastructures such as power, traffic, and health-care networks and smart cities rely on advances in CPS. However, higher connectivity increases the vulnerability of CPS because it exposes them to threats from both the cyber domain and the physical domain. An attack or a fault within the cyber or physical domain can subsequently affect the cyber domain, the physical domain, or both, resulting in anomalies. An attack or a fault on CPS can have serious or even lethal consequences. Traditional anomaly diagnosis techniques mainly focus on cyber-to-cyber or physical-to-physical interactions. However, in practice they can often be subverted in the face of cross-domain attacks or faults. In summary, the safety and reliability of CPS become more and more crucial every day and existing techniques to diagnose or mitigate CPS attacks and faults are not sufficient to eliminate vulnerability. The motivation of this dissertation is to enhance anomaly diagnosis and mitigation for CPS, covering physical-to-physical and cyber-to-physical attacks or faults. With the advantage of dealing with system uncertainties and providing system state estimation, observer-based anomaly diagnosis is of great interest. The first task is to design a multiple observers framework to diagnose sensor anomalies for continuous systems. Since CPS contain both continuous and discrete variables, CPS are modeled as hybrid systems. Utilizing the relationship between the continuous and discrete variables, a conflict-driven hybrid observer-based anomaly detection method is proposed, which checks for conflicts between the continuous and discrete variables to detect anomalies. Lastly, the observer design for hybrid systems is improved to enable observer-based anomaly diagnosis for a wider class of hybrid systems. The novel observer-based anomaly diagnosis and mitigation approaches introduced in this dissertation can not only diagnose anomalies caused by traditional faults, but also anomalies caused by sophisticated attacks. This research work can benefit the overall security of critical infrastructures, preventing disastrous consequences and reducing economic loss. The effectiveness of the proposed approaches is demonstrated mathematically and illustrated through applications to various simulated systems, including a suspension system, the Positive Train Control system and a microgrid system.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147576/1/zhengwa_1.pd

A Framework for Life Cycle Cost Estimation of a Product Family at the Early Stage of Product Development

A cost estimation method is required to estimate the life cycle cost of a product family at the early stage of product development in order to evaluate the product family design. There are difficulties with existing cost estimation techniques in estimating the life cycle cost for a product family at the early stage of product development. This paper proposes a framework that combines a knowledge based system and an activity based costing techniques in estimating the life cycle cost of a product family at the early stage of product development. The inputs of the framework are the product family structure and its sub function. The output of the framework is the life cycle cost of a product family that consists of all costs at each product family level and the costs of each product life cycle stage. The proposed framework provides a life cycle cost estimation tool for a product family at the early stage of product development using high level information as its input. The framework makes it possible to estimate the life cycle cost of various product family that use any types of product structure. It provides detailed information related to the activity and resource costs of both parts and products that can assist the designer in analyzing the cost of the product family design. In addition, it can reduce the required amount of information and time to construct the cost estimation system