APPLICATION OF HYBRID OBSERVATION TECHNIQUES FOR FAULT DIAGNOSIS OF ROTATING MACHINES

필탄 파르진 The Graduate School of the University of Ulsan Dep 2020 국내박사

The fault diagnosis of industrial facilities is one of the significant and ever-growing fields of research. Fault diagnosis can be applied to a diversity of industrial components such as rotary machines, motors, pipelines, robot manipulators, gearboxes, etc. In this research, hybrid approaches are developed for detection and classification of the rotary machine bearing faults. Rolling element bearing represents a class of nonlinear and multiple-degrees-of-freedom rotating machines that have pronounced coupling effects and can be used in various industries. Uncertain conditions in which a rolling element bearing operates, as well as nonlinearities, represent challenges for fault diagnosis that are addressed through the fault diagnosis techniques. If defects in the rolling element bearing are not identified and diagnosed in time they can lead to the failure of the whole mechanical system. The failure of the rolling element bearing results in unexpected downtimes and great economic losses. Moreover, it can be a threat to the safety of the people working in the facility. The condition monitoring of a rolling element bearing can be achieved through different techniques. This work focuses on vibration and acoustic emission analysis method because these signals are suitable for fault diagnosis in rolling element bearing. Several methods have been advised for anomaly detection and identification in rolling element bearings. These techniques can be divided into four principal divisions: model-based techniques, signal-based approaches, data-driven algorithms, and hybrid-based procedures. In this dissertation, hybrid-based techniques that uses a combination of the system modeling algorithms, observation techniques, and a machine learning-based classification are introduced for the diagnosis of bearing faults of various severities. System modeling is the main argument in designing observation-based techniques for fault diagnosis. Numerous procedures have been used to model bearings and can be classified into two main groups: mathematical-based system modeling, and system identification techniques. The mathematical-based bearing modeling such as five-degrees-of-freedom mathematical modeling of vibration signals, and system identification techniques such as ARX-Laguerre and fuzzy ARX-Laguerre bearing vibration and acoustic emission signal modeling are prescribed in this work. The model-based fault diagnosis techniques are reliable and robust algorithms and have been used in various applications. Observation-based algorithms are the main model-based techniques used for bearing fault diagnosis. Despite the advantages of observation-based approaches, these techniques have some limitations in the presence of uncertain and unknown conditions. Nonlinear-based observation techniques (e.g., sliding mode observer, feedback linearization observer) and linear-based observation algorithms (e.g., proportional-integral (PI) observer) are the main procedures used to develop observation to estimate the signals. The sliding mode observer is a nonlinear and high-gain observer that can improve a system's dynamic and reduce the estimator error infinite time. This technique is robust and reliable, but is prone to chattering phenomenon and limited estimation accuracy. To minimize the chattering phenomenon, the higher-order sliding mode observer is recommended in this work. This technique suffers from a somewhat reduced estimation accuracy. To improve the estimation accuracy, a higher-order super-twisting sliding mode observer was developed. Sliding mode observer and high-order super twisting (extended-state) sliding mode observer have acceptable state estimation and works in uncertain condition; however, chattering phenomenon is the main drawback of these techniques in uncertain conditions. To minimize the effect of the chattering phenomenon, a feedback linearization observer was developed. The feedback linearization observer is a powerful technique for signal estimation. The main idea of this approach is to algebraically transform the nonlinear system dynamic parameters into a linearized system so that the feedback observation algorithm can be applied. This observer is based on the dynamics of the system's behavior, thus it works perfectly if all parameters are known. Apart from the stability and reliability of this observation technique, it suffers from a lack of robustness. To address this issue, the variable structure (extended-state) feedback linearization observer is developed in this work. Despite the advantages of high-order super-twisting sliding mode observer and variable structure feedback linearization observer for fault diagnosis of bearing based on five-degrees-of-freedom mathematical modeling of vibration signals such as reliability and robustness, these techniques have some limitations in the presence of uncertain and unknown conditions. To decrease these limitations, the auto-regressive exogenous input (ARX) technique is advised for bearing system modeling in this work. To improve the stability and robustness of ARX modeling for vibration/acoustic emission signals, an orthonormal function technique based on the ARX-Laguerre method is developed. Moreover, The ARX-Laguerre PI observer is a linear and easy to implement technique for signals estimation but have limited robustness and accuracy. To address these issues, an extended-state technique based on a sliding mode algorithm is applied to the ARX-Laguerre PI observer to perform fault diagnosis and overcome potential problems that may appear when applying a linear observer to a nonlinear signal. Moreover, the simplicity and flexibility of the ARX-Laguerre extended-state PI observation method allow it to be applied in industrial environments for single-type and multiple-type fault diagnosis of bearing. The ARX-Laguerre technique is robust and stable, but has some limitations when applied to nonlinear and non-stationary signal modeling. To address these problems, a fuzzy ARX-Laguerre technique for vibration and acoustic emission bearing signals is prescribed in this work. Through the high-order super-twisting (extended-state) sliding mode observer increases the robustness and reduces the chattering phenomenon, this scheme, unfortunately, suffers from the small rate chattering phenomenon and signal estimation accuracy in the presence of uncertainties and unknown conditions. Therefore, in this dissertation, the fuzzy technique is applied to the fuzzy ARX-Laguerre high-order super-twisting (extended-state) sliding mode observer to increases the signal estimation accuracy and design fuzzy ARX-Laguerre fuzzy high-order super-twisting (fuzzy extended-state) sliding mode observer. Once the rotary machinery bearing is modeled based on a mathematical-based modeling (e.g., five-degrees-of-freedom mathematical modeling of vibration signals) or system identification techniques (e.g., ARX-Laguerre technique, and fuzzy ARX-Laguerre method), and the rotary machinery bearing signals are estimated based on the extended-state observers (e.g., high-order super-twisting sliding mode observer, variable structure feedback linearization observer, and ARX-Laguerre sliding mode PI observer) or fuzzy extended-state observer (e.g., fuzzy ARX-Laguerre fuzzy high-order super-twisting sliding mode observer), the decision regarding the bearing conditions can be made. In this work, machine learning-based classification techniques called a support vector machine (SVM) and decision tree (DT) are employed the decision-making procedure for bearing fault diagnosis to complete the proposed techniques for diagnosis the faults. Specifically, during the experiment the high-order super-twisting sliding mode observer, variable structure feedback linearization observer, ARX-Laguerre sliding mode PI observer, and fuzzy ARX-Laguerre fuzzy high-order super-twisting sliding mode observer an average fault diagnosis accuracy of 95.8%, 96.1%, 94.3%, and 99.2%, respectively.

The Effect of Mobile Shopping App Design Quality and Mobile Service Quality on Behavioral Intention in the Electronic Commerce Industry

Liu Peipei The Graduate School of the University of Ulsan Dep 2021 국내석사

모바일 인터넷 환경에서 쇼핑은 더 이상 오프라인 매장과 웹사이트 쇼핑에 국 한되지 않는다.App 쇼핑은 전자상거래 발전에 나타난 새로운 쇼핑 채널이다. 모 바일 쇼핑 앱을 연구하는 App 디자인 품질과 서비스 품질은 휴대폰 정보 소비 촉 진에 긍정적인 역할을 한다. 지금까지의 연구는 서비스 품질이 고객 행동의도에 미치는 영향을 주로 연구했으며, 모바일 앱 디자인 품질과 서비스 품질이 고객 행동의도에 미치는 영향에 대한 연구는 거의 없었다. 그러나 모바일 쇼핑은 오늘 날의 주류 쇼핑 트렌드이다. 따라서 가장 인기 있고 인기 있는 모바일 쇼핑 앱이 될 수 있는 방법을 연구하 는 것이 바로 그 연구 의도다. 연구 문제는 모바일 쇼핑 앱의 디자인 품질이 모 바일 만족도에 정향적인 영향을 미치는가 하는 점이다. 모바일 서비스 품질이 모 바일 서비스 만족도에 정향적으로 영향을 미치고 있습니까? 모바일 쇼핑 앱 디자 인 품질이 모바일 신뢰에 포지티브 영향을 미치는가? 모바일 서비스 품질이 모바 일 신뢰에 긍정적인 영향을 미치고 있습니까? 모바일 만족도가 모바일 신뢰에 포 지티브 영향을 미치고 있습니까? 모바일 만족도가 고객의 행동 의향에 정향적으 로 영향을 미치는가? 모바일 신뢰가 고객의 행동 의사에 정향적인 영향을 미치는 가? 모바일 쇼핑 앱 디자인 품질이 행동 의향에 정향적인 영향을 미치는가? 모바 일 서비스 품질이 행동의향에 영향을 미칩니까? 이 기사에서는 이러한 질문에 답 하기 위해 전인 연구를 바탕으로 아홉 가지 가설과 연구 모형을 제시했습니다. 이러한 측정은 선행 문헌에 기초하여 개발되었다. 설문조사는 중국에서 소셜 소프트웨어인 위챗과 텐센트 등의 온라인 조사를 통해 수집하였다. 전자서비스 품질이 고객 행동의도에 직접적인 영향을 미치는 반면 모바일 쇼핑앱의 디자인 품질은 고객의 행동의도에 직접적인 영향을 미치지 못한다는 연구결과가 나왔다. 또한 모바일 쇼핑 앱의 디자인 품질은 전자 만족도와 정연하게 연관돼 있다. 전 자만족도는 디자인 품질과 행동 의도에도 중개 역할을 한다. 하지만 디자인 품질 은 신뢰에 직접적인 영향을 미칠 수 없고, 신뢰도 모바일 쇼핑 앱 디자인 품질과 고객 행동의도 사이에서 중개 역할을 할 수 없다. 그러나 전자 만족도는 신뢰에 긍정적인 영향을 미친다. 신뢰가 서비스 품질과 행동의도 간의 관계에 조절 역할 을 한다는 연구결과도 나왔다. 본연구의 결과는 모바일 쇼핑 애플리케이션의 개발자와 관리자가 고객 경험을 개 선할 수 있는 것을 더 잘 이해하고, 모바일 쇼핑 앱에 대한 고객의 재방문 의도 를 증가시키는 데 도움이 될 수 있다. In the mobile Internet environment, shopping is no longer limited to physical stores and website shopping. Shopping by Apps is a new shopping channel emerging in the development of M-commerce. Studying the App design quality and mobile service quality has a positive effect on promoting mobile consumption. Previous studies mainly studied the effect of service quality on customer behavioral intention, and few studies on the impact of mobile APP design quality and mobile service quality on customer behavioral intention. However, mobile shopping is the mainstream shopping trend today. Therefore, study how to become the most popular and frequently-visited mobile shopping APP is this research intention. The research question is whether mobile shopping APP design quality has a positive effect on M-satisfaction? Whether M-service quality has a positive effect on M-satisfaction? Whether mobile shopping APP design quality has a positive effect on M-trust? Whether M-service quality has a positive effect on M- trust? Whether M-satisfaction has a positive effect on M-trust? Whether M-satisfaction has a positive effect on customer behavioral intention? Whether M-trust has a positive effect on customer behavioral intention? Whether Mobile shopping APP design quality has a positive effect on behavioral intention? Whether M-service quality has a positive effect on behavioral intention? To answer these questions, based on previous studies, nine hypotheses and research model have been proposed. The measurements were developed based on previous literature and questionnaire surveys were collected in China through online surveys such as social software WeChat and Tencent. The outcomes show that the M-service quality has a direct effect on the customer behavioral intention, while the mobile shopping APP design quality is not. But M-satisfaction as a mediator can affect the relationship between the mobile shopping APP design quality and behavior intention. Meanwhile, the mobile shopping APP design quality has a positive correlation with M-satisfaction. And then, the mobile shopping APP design quality cannot affect trust, and trust also cannot mediate the relationship between the mobile shopping APP design quality and customer behavioral intention. Furthermore, M-satisfaction has a positive effect on trust. Finally, the research findings also suggest that trust can mediating the relationship between M-service quality and behavioral intention. The findings of this paper can help developers and managers of mobile shopping APPs to better understand what can improve customer experience and increase customers revisit intention for their mobile shopping APPs.