Machine Fault Diagnosis and Condition Prognosis using Adaptive Neuro-Fuzzy Inference System and Classification and Regression Trees : 기계 결함진단 및 예지를 위한 ANFIS 와 CART

다국어 초록 (Multilingual Abstract)

Sustaining the productivity is a key strategy of manufacturers to exist on the drastic competition of global market. In order to keep up the productivity, manufacturers need to reduce the manufacturing costs by using maintenance due to its major part of the total costs of the manufacturing process. Consequently, a good maintenance strategy plays a crucial role in the existence and development of the organizations. Additionally, in accompany with the fast development of technology, the equipment becomes more and more complex. The traditional maintenance strategies such as corrective maintenance and prescheduled maintenance cannot guarantee the functional operation of equipments and are progressively replaced by intelligent maintenance strategies in which condition based maintenance is one of the delegates.
Condition-based maintenance has been defined as maintenance actions which are based on actual conditions of equipments obtained from nondestructive inspections, operations and condition measurements. This means that the equipment condition is accessed under operation for making conclusions whether that equipment will be failed and the effective maintenance actions are necessary to avoid the consequences of that failure or not. The use of condition-based maintenance systems ensures that the condition of equipment is always monitored and alarm limitations can be indicated if the condition exceeds predefined levels. In condition-based maintenance system, fault diagnosis and condition prognosis are crucial components which have been considerably received much attention from the community of researchers and maintainers. Fault diagnosis is the ability to detect fault, isolate the component which is failure, and decide on the potential impact of failed component on the health of the system; while condition prognosis is defined as a capability to foretell the future states, predict the remaining useful life ？ the time left for the normal operation of machine before breakdowns occur or machine condition reaches the critical failure value.
In this study, classification and regression trees (CART) and adaptive neuro-fuzzy inference systems (ANFIS) will be developed as an effective intelligent system for performing machine fault diagnosis and condition prognosis. CART is known as one of the illustrious techniques of the decision tree induction and used for the purpose of either classification or regression depending on the output variable which is categorical or numerical. CART recursively partitions the entire data into binary descendant subsets which are as homogeneous as possible with respect to the response variables. High effective computation and reliability are the remarkable advantages of this algorithm. In the second technique, ANFIS is an excellent integration of the adaptive capability of neural networks and the modeling human knowledge ability of fuzzy logic. During the learning process, the parameters of fuzzy membership functions initially determined by experts are adapted to the relationship between the input and output. That combination makes the ANFIS model more systematic and less dependent on the expert knowledge.
For implementing the fault diagnosis, CART and ANFIS are combined with another technique so-called feature-based technique. This technique is one of the powerful techniques to represent the raw data as features which are representatives of values indicating the machine condition. By using features, the encountered problem in data transfer and data storage could be effortlessly solved. Feature-based technique consists of data acquisition, data preprocessing, feature representation, feature extraction, feature selection and classifiers. In the proposed system for fault diagnosis, CART is used as a feature selection tool to select pertinent features which can characterize the machine conditions from the whole feature set whilst ANFIS plays a role as a classifier. In order to be evaluated, this system is applied to diagnose the faults of induction motor, which is an indispensable part in several industrial applications. The high performance results indicate that this system offers a potential for machine fault diagnosis.
Foretelling the future states of machine has become more and more significant in modern industry. It assists maintainers or system operators in monitoring, inspecting the machines？ operating conditions, and detecting the incipient faults so that they could opportunely perform remedial actions to avoid the catastrophic failures. Furthermore, it enables the scheduled maintenance to be more effective. In this study, the future machines？ operating conditions are predicted by using CART and ANFIS model in combination with time series techniques. These time series techniques consist of methods which are utilized to determine the optimal observations and the steps ahead as the inputs and outputs of predictors, respectively. The trending data of a low methane compressor is used to validate the proposed method. The predicted results show that CART and ANFIS predictors are reliable and promising tools in machine condition prognosis.

국문 초록 (Abstract)

세계 시장 경쟁체제에서 생산성은 중요한 경영전략이다. 경영자들은 생산성을 유지하기 위해서 전체생산비용에서 많은 부분을 차지하는 설비유지보수 전략을 이용하여 생산비용을 줄이는 것이 필요하다. 다시 말해서, 좋은 설비유지보수 전략이 현재 중요한 역할을 하고 있다. 게다가 빠른 기술발전과 함께, 기계들은 계속해서 복잡해지고 있다. 과거의 사후정비, 예방정비는 기계의 기능적인 작동을 보장할 수 없고, 대표적인 상태기반정비인 인공지능 기법을 도입한 설비유지보수로 점차 대체되어가고 있다.
상태기반 정비는 비파괴검사, 기계작동과 상태로부터 실제 기계의 상태에 근거하여 설비유지보수를 하는 것으로 정의된다. 이것은 기계의 파손이 일어나지 않도록 기계의 파손여부와 효과적인 정비방법에 대한 의사결정을 할 수 있다는 것이다. 상태기반의 정비를 사용한다는 것은 기계상태가 항상 모니터링(Monitoring)되고, 미리 설정된 알람(Alarm) 레벨을 나타낼 수 있어야 한다. 상태기반정비에서 결함진단과 예지는 연구자들과 엔지니어들에게 관심받을만한 중요한 요소이다. 결함진단은 결함을 감지하는 것으로 시스템의 잠재적인 결함요소를 결정하는 기술이다. 그리고 예지진단은 기계의 미래의 상태에 대해서 정상상태부터 파손이 일어나기 전까지의 잔여유용수명을 예측할 수 있는 것으로 정의된다.
이 연구에서는 기계결함진단과 예를 위해 CART(Classification and Regression Trees)와 ANFIS(Adaptive Neuro-fuzzy Inference System)가 개발되었다. CART는 결정목(Decision Tree) 기술중의 하나이고 명확하고 수치적인 결과변수에 의존하여 분류하거나 회귀할 목적으로 사용한다. CART는 전체 데이터를 반복적으로 반응할 변수에 대해 가능한 상동하도록 이진 하위집합을 분할한다. 이 알고리즘은 효율적인 계산과 신뢰성이 장점이다. 다음으로 ANFIS 는 뉴럴 네트워크의 적응능력을 통합할수 있고, 퍼지논리의 인간지식을 모델링할 수 있다. 학습하는 동안에는 전문가에의해 초기화된 퍼지 멤버십 함수의 파라미터는 입력과 출력 사이의 관계로 적응하게 된다. 그것이 ANFIS 모델을 시스템적이고 전문가 지식에 의존하지 않게 만드는 요소이다.
결함진단에 사용하기 위해 CART와 ANFIS는 특징기반의 기술과 결합하였다. 이 기술은 원래의 신호를 기계의 상태를 나타낼 수 있는 특징값으로 나타내는 강력한 기법이다. 특징을 이용하여 데이터 전송과 저장문제가 효과적으로 해결된다. 특징기반의 기술은 데이터취득, 데이터 전처리, 특징계산, 특징추출, 특징선택과 분류로 구성되어 있다. 결함진단을 위해 제안된 CART는 기계상태를 특징지을 수 있는 특징선택에 사용되었고 ANFIS는 특징분류에 사용되었다. 이 기술을 평가하기 위해 산업계에서 아주 중요하게 사용되고 있는 유도전동기에 적용하였다. 고성능의 분류결과는 기계결함진단에 잠재력이 있다고 판단된다.
기계의 미래상태를 예지하는 것은 현대 산업계에서 더욱더 중요해지고 있다. 그것은 엔지니어, 시스템관리자에게 도움을 주며, 재해를 예방할 수 있는 기회를 제공한다. 게다가 설비유지보수 계획을 할 때 더욱 효과적으로 할 수 있다. 이 연구에서는 미래의 기계상태를 CART와 ANFIS를 시간신호 예측에 사용하였다. 이 기술은 예측할 때 최적의 예측구간을 결정하는 방법을 포함하였다. 메탄 압축기의 경향데이터는 제안된 시스템을 검증하는 좋은 예이다. 예측된 결과는 CART와 ANFIS가 예지도구로서 신뢰할만한 도구인 것을 입증한다.

목차 (Table of Contents)

• Ⅰ. Introduction = 1
• 1. Background = 1
• 2. Motivation of This Research = 6
• 3. Research Objectives = 6
• 4. Tools and Approaches = 7
• 5. Scientific Contribution of This Research = 7
• 6. Organization of Thesis = 8
• References = 9
• Ⅱ. The State-of-The-Art of Machine Fault Diagnosis and Prognosis = 11
• 1. Machine Fault Diagnosis = 11
• 1.1. Model-based approaches = 11
• 1.2. Knowledge-based approaches = 13
• 1.3. Pattern recognition-based approaches = 15
• 2. Machine Fault Prognosis = 19
• 2.1. Statistical approaches = 20
• 2.2. Model-based approaches = 21
• 2.3. Data-driven based approaches = 22
• References = 22
• Ⅲ. Background Knowledge = 36
• 1. Feature-Based Diagnosis and Prognosis: a Review = 36
• 1.1. Feature extraction techniques = 37
• 1.2. Feature selection techniques = 39
• 2. Feature Representation = 40
• 2.1. Features in time domain = 40
• 2.1.1. Cumulants = 40
• 2.1.2. Upper and lower bound histogram = 44
• 2.1.3. Entropy estimation and error = 45
• 2.1.4. Auto-regression coefficients = 45
• 2.2. Feature in frequency domain = 46
• 2.2.1. Fourier transform = 46
• 2.2.2. Spectral analysis = 47
• 2.2.3. Frequency parameter indices = 48
• 3. Classification and Regression Trees (CART) = 49
• 3.1. Introduction = 49
• 3.2. Tree growing = 50
• 3.2.1. Classification tree = 50
• 3.2.2. Regression tree = 52
• 3.3. Tree pruning = 54
• 3.3.1. Classification tree = 54
• 3.3.2. Regression tree = 55
• 3.4. Cross-validation for selecting the best tree = 56
• 4. Adaptive Neuro-Fuzzy Inference System (ANFIS) = 57
• 4.1. Architecture of ANFIS = 57
• 4.2. Learning algorithm of ANFIS = 60
• 5. Conclusions = 61
• References = 61
• Ⅳ. CART and ANFIS Based Fault Diagnosis for Induction Motors = 67
• 1. Introduction = 67
• 2. Induction Motor Faults = 67
• 2.1. Bearing faults = 70
• 2.2. Stator or armature faults = 72
• 2.3. Broken rotor bar and end ring faults = 74
• 2.4. Eccentricity related faults = 75
• 3. The Proposed Fault Diagnosis System for Induction Motors = 77
• 3.1. Experiment and data acquisition = 79
• 3.2. Feature calculation = 81
• 3.3. Feature selection and classification = 83
• 4. Conclusion = 90
• References = 91
• Ⅴ. Machine Condition Prognosis = 94
• 1. Introduction = 94
• 2. Prediction Strategies = 97
• 2.1. Recursive prediction strategy = 97
• 2.2. DirRec prediction strategy = 98
• 2.3. Direct prediction strategy = 98
• 3. Time Delay Estimation = 99
• 4. Determining Embedding Dimension = 100
• 4.1. Cao's method = 100
• 4.2. False nearest neighbor method (FNN) = 101
• 5. Proposed System for Machine Condition Prognosis = 103
• 6. Experiment = 105
• 7. Case Studies of Machine Condition Prognosis = 108
• 7.1. Case study 1: CART and OS prediction = 108
• 7.2. Case study 2: parallel CART and MS direct prediction = 115
• 7.2.1. Parallel structure of CART = 115
• 7.2.2. Results and discussions = 116
• 7.3. Case study 3: ANFIS and MS direct prediction = 124
• 8. Conclusions = 130
• References = 132
• Ⅵ. Conclusions and Future Works = 134
• 1. Conclusions = 134
• 2. Future Works = 135
• Acknowledgements = 140