Vibration Signal Analysis for the Lifetime-Prediction and Failure Detection of Future Turbofan Components

Planetary gearbox and hydrodynamic journal bearings (HJB) are going to be integrated in future turbofan engines. This paper presents the results of applied methods to detect failures of these components. At first, failure detection requirements are derived by using system engineering techniques. In consideration of the identified failures theoretical assumptions are discussed and subsequently verified. Vibration and acoustic emission (AE) sensors seem promising to detect failures in an early stage. To prove the theoretical considerations experiments are carried out on test benches. Tooth flank damage of a planet gear in a planetary gearbox design is investigated. High demands are placed on the signal processing due to design-related amplitude modulation effects. Vibrations are measured using acceleration and AE sensors, which are mounted on the ring gear. The investigated failure type leads to excitation of non-stationary AE signals. It is proposed that the AE signals have a cyclostationary characteristic. Using cyclostationary-based processing techniques the signal’s hidden periodicities can be revealed. A separated analysis of each planet and evaluation of the envelope spectrum finally allows the detection of this failure type. Instead of roller bearings, HJB can be integrated in planet gears. The most essential damaging mechanism for HJB is wear as a result of mixed or boundary friction. These friction states are caused by conditions like Start/Stop Cycles, insufficient oil supply, overload or oil contamination. The accumulated intensity and duration of friction can be a measure of the remaining useful lifetime (RUL). To estimate the RUL friction has to be differentiated regarding the intensity. AE technology is a promising method to detect friction in HJB. Therefore, AE signals of the mentioned conditions are acquired. Due to rotating planet gears there is no possibility to place AE sensors directly on the surface of HJB. Finally suitable features for both components are extracted from the processed signals. Their separation efficiency with respect to the failure types is evaluated

환경 및 운행적 불확실성에 강건한 유성 기어박스의 진동기반 고장진단

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 윤병동.Vibration-based fault diagnosis of planetary gearboxes can effectively prevent many undesired failures and thereby reduce the maintenance costs of large-scale engineering systems. However, this type of fault diagnosis is often challenging due to various uncertainties, such as the uncertain operating conditions that affect the vibration characteristics of the gearbox. To cope with the uncertainty-related challenges of vibration-based fault diagnosis of planetary gearboxes, this thesis presents three research thrusts: 1) quantitative definition of the stationary operating condition of a gearbox, 2) data-efficient fault diagnosis using autocorrelation-based time synchronous averaging (ATSA), and 3) tooth-wise fault identification using a health data map (HDmap), without the use of an encoder system. The first research thrust presents a class-wise fault diagnosis methodology to solve the challenges that arise from the uncertain operating conditions of a gearbox. In the proposed method, the operating condition of the gearbox is quantitatively divided into multiple classes in such a way that the vibration signals in each class are homogeneous. The second research thrust presents a data-efficient time synchronous averaging (TSA) method for a planetary gearbox. To enhance the signal-to-noise ratio, conventional TSA for a planetary gearbox extracts the vibration signals using a narrow-range window function, which requires a significant amount of stationary vibration signals. However, in practice, stationary vibration signals are rarely obtainable due to the uncertain operating conditions of the system. In this research, an autocorrelation function is used to extend the range of the window function to enable reliable fault diagnosis, even with a small amount of stationary vibration signals. The third research thrust proposes an original idea for tooth-wise fault identification of a planetary gearbox. The proposed method is based on a health data map that can be used even with uncertain vibration characteristics. The two-dimensional health data map can sketch the health data corresponding to every pair of gear teeth to isolate the location of the faulty gear tooth. In addition, a Hilbert transform-based phase estimation technique is employed for an encoder-less health data map that is suitable even under the slightly varying rotational speed.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Scope of Research 2 1.3 Structure of the Thesis 6 Chapter 2. Technical Background and Literature Review 7 2.1 Fault Diagnosis of a Gearbox uncer the Varying Operating Condition 7 2.2 Fault Diagnosis of a Spur Gearbox 10 2.2.1 Time Synchronous Averaging 10 2.2.2 Definition of Residual Signal and Difference Signal 13 2.2.3 Definition of Health Data (HD) 14 2.2.4 Local Meshing Plane 20 2.3 Fault Diagnosis of a Planetary Gearbox 22 2.3.1 Dynamics Characteristics of Planetary Gearbox 22 2.3.2 Time Synchronous Averaging for a Planetary Gearbox with Window Function 26 2.4 Summary and Discussion 31 Chapter 3. Analytical Model and Testbed for Planetary Gearbox 37 3.1 Analytical Model for Planetary Gearbox 37 3.1.1 Analytical Model for Normal Condition 37 3.1.2 Examining Faults for the Analytical Model 39 3.2 Testbed for Planetary Gearbox 41 Chapter 4. Quantitative Definition of the Stationary Operating Condition 44 4.1 Analytical Modeling of Wind Turbine (WT) Performance 45 4.2 Mathematical Derivation of the PDF of Power and Rotor Speed 48 4.2.1 The PDF of Power 49 4.2.2 The PDF of Rotor Speed 52 4.3 Classification of the Operating Conditions of a WT 54 4.3.1 A Classification Method for Operating Conditions of a WT 54 4.3.2 Definition of Quantitative Classification Criteria 57 4.4 Case Studies 63 4.4.1 Case Study with the Analytical WT Model 64 4.4.2 Case Study with a 2.5 Megawatt Wind Turbine 66 4.5 Validation Study for Classification of Stationary Operating Conditions 69 4.5.1 Homogeneity Evaluation of the Vibration Signals 70 4.5.2 Vibration-based Condition Monitoring 71 4.6 Summary and Discussion 73 Chapter 5. Autocorrelation-based Time Synchronous Averaging (ATSA) 76 5.1 Monitoring Position and Meshing Tooth of Planet Gears 76 5.2 In-depth Study on the Autocorrelation Function for Vibration Signals 78 5.3 Autocorrelation-based TSA 81 5.3.1 Representative Autocorrelation Function 81 5.3.2 Design of the Window Function 82 5.3.3 Application of Window Function for TSA 85 5.4 Case Studies 87 5.4.1 Case Study #1: Analytical Model 88 5.4.2 Case Study #2: Testbed 89 5.5 Summary and Discussion 92 Chapter 6. Tooth-wise Fault Identification of Gearbox using Health Data 94 6.1 TSA and Difference signal for One Hunting Tooth Cycle (HTC) in Sample Domain 95 6.2 Health Data for One Hunting Tooth Cycle (HTC) in Sample Domain 97 6.3 Health Data (HD) in Tooth Domain: HDmap 101 6.4 Encoder-less Health Data Map 104 6.5 Case Study 106 6.5.1 Case Study #1: Analytical Model 106 6.5.2 Case Study #2: Testbed 111 6.6 Summary and Discussion 120 Chapter 7. Conclusions 122 7.1 Contributions and Significance 122 7.2 Suggestions for Future Research 124 References 128 Abstract (Korean) 143Docto

Development of effective gearbox fault diagnosis methodologies utilising various levels of prior knowledge

Effective fault diagnosis techniques are important to ensure that expensive assets such as wind turbines can operate reliably. Vibration condition monitoring data are rich with information pertaining to the dynamics of the rotating machines and are therefore popular for rotating machine diagnostics. However, vibration data do not only contain diagnostic information, but operating condition information as well. The performance of many conventional fault diagnosis techniques is impeded by inherent varying operating conditions encountered in machines such as wind turbines and draglines. Hence, it is not only important to utilise fault diagnosis techniques that are sensitive to faults, but the techniques should also be robust to changes in operating conditions. Much research has been conducted to address the many facets of gearbox fault diagnosis e.g. understanding the interactions of the components, the characteristics of the vibration signals and the development of good vibration analysis techniques. The aforementioned knowledge, as well as the availability of historical data, are regarded as prior knowledge (i.e. information that is available before inferring the condition of the machine) in this thesis. The available prior knowledge can be utilised to ensure that e ective gearbox fault diagnosis techniques are designed. Therefore, methodologies are proposed in this work which can utilise the available prior knowledge to e ectively perform fault diagnosis, i.e. detection, localisation and trending, under varying operating conditions. It is necessary to design di erent methodologies to accommodate the di erent kinds of historical data (e.g. healthy historical data or historical fault data) that can be encountered and the di erent signal analysis techniques that can be used. More speci cally, a methodology is developed to automatically detect localised gear damage under varying operating conditions without any historical data being available. The success of the methodology is attributed to the fact that the interaction between gear teeth in a similar condition results in data being generated which are statistically similar and this prior knowledge may be utilised. Therefore, a dissimilarity measure between the probability density functions of two teeth can be used to detect a gear tooth with localised gear damage. Three methodologies are also developed to utilise the available historical data from a healthy machine for gearbox fault diagnosis. Firstly, discrepancy analysis, a powerful novelty detection technique which has been used for gear diagnostics under varying operating conditions, is extended for bearing diagnostics under varying operating conditions. The suitability of time-frequency analysis techniques and di erent models are compared for discrepancy analysis as well. Secondly, a methodology is developed where the spectral coherence, a powerful second-order cyclostationary technique, is supplemented with healthy historical data for fault detection, localisation and trending. Lastly, a methodology is proposed which utilises narrowband feature extraction methods such as the kurtogram to extract a signal rich with novel information from a vibration signal. This is performed by attenuating the historical information in the signal. Sophisticated signal analysis techniques such as the squared envelope spectrum and the spectral coherence are also used on the novel signal to highlight the bene ts of utilising the novel signal as opposed to raw vibration signal for fault diagnosis. Even though a healthy state is the desired operating condition of rotating machines, fault data will become available during the operational life of the machine. Therefore, a methodology, centred around discrepancy analysis, is developed to utilise the available historical fault data and to accommodate fault data becoming available during the operation of the machine. In this investigation, it is recognised that the machine condition monitoring problem is in fact an open set recognition problem with continuous transitions between the healthy machine condition and the failure conditions. This is explicitly incorporated into the methodology and used to infer the condition of the gearbox in an open set recognition framework. This methodology uses a di erent approach to the conventional supervised machine learning techniques found in the literature. The methodologies are investigated on numerical and experimental datasets generated under varying operating conditions. The results indicate the bene ts of incorporating prior knowledge into the fault diagnosis process: the fault diagnosis techniques can be more robust to varying operating conditions, more sensitive to damage and easier to interpret by a non-expert. In summary, fault diagnosis techniques are more e ective when prior knowledge is utilised.Thesis (PhD)--University of Pretoria, 2019.Mechanical and Aeronautical EngineeringPhDUnrestricte

Unconditionally convergent time domain adaptive and time-frequency techniques for epicyclic gearbox vibration

Condition monitoring of epicyclic gearboxes through vibration signature analysis, with particular focus on time domain methods and the use of adaptive filtering techniques for the purpose of signal enhancement, is the central theme of this work. Time domain filtering methods for the purpose of removal of random noise components from periodic, but not necessarily stationary or cyclostationary, signals are developed. Damage identification is accomplished through vibration signature analysis by nonstationary timefrequency methods, belonging to Cohen’s general class of time-frequency distributions, strictly based in the time domain. Although a powerful and commonly used noise reduction technique, synchronous averaging requires alternate sensors in addition to the vibration pickup. For this reason the use of time domain techniques that employ only the vibration data is investigated. Adaptive filters may be used to remove random noise from the nonstationary signals considered. The well-known Least Mean Squares algorithm is employed in an adaptive line enhancer configuration. To counter the much discussed convergence difficulties that are often experienced when the least mean squares algorithm is applied, a new unconditionally convergent algorithm based on the spherical quadratic steepest descent method is presented. The spherical quadratic steepest descent method has been shown to be unconditionally convergent when applied to a quadratic objective function. Time-frequency methods are succinctly employed to analyse the vibration signals simultaneously in the time and frequency domains. Transients covering a wide frequency range are a clear and definite indication of impacting events as gear teeth mate, and observation of such events on a timefrequency distribution are used to indicate damage to the transmission. The pseudo Wigner-Ville distribution and the Spectrogram, both belonging to Cohen’s general class of time-frequency distributions are comparatively used to the end of damage identification. It is shown that an unconditionally convergent adaptive filtering technique used in conjunction with time-frequency methods can indicate a damaged condition in an epicyclic gearbox, where the non-adaptively filtered data did not present clear indications of damage.Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007.Mechanical and Aeronautical EngineeringMEngMEngunrestricte

풍력발전 기어박스의 진동 기반 고장진단 프레임워크

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2013. 8. 윤병동.최근 풍력발전기의 신뢰성 문제가 풍력에너지 산업에서 큰 이슈가 되고 있다. 특히 풍력발전기의 기어박스는 유지보수 비용이 크기 때문에, 풍력발전기의 부품 중에서 경제적 위험도가 가장 크다고 평가되고 있다. 지금까지 풍력발전기의 신뢰성을 보장하기 위한 수많은 연구가 진행되었음에도 불구하고, 아직까지 해당 연구 분야는 여러 가지 어려운 문제점에 직면해 있다. 대표적으로 크게 1) 비정상 (non-stationary) 운행 상태로 인해 발생하는 고장진단 기술의 어려움, 2) 특정 풍력발전 단지 내에 수많은 센서로부터 계측되는 방대한 양의 데이터와 등으로 나눌 수 있다. 따라서 본 연구에서는 일반적인 고장진단 과정을 포괄하는 기어박스의 고장진단 프레임워크를 제안한다. 제안된 프레임워크는 방대한 양의 데이터를 효율적으로 관리하는 동시에 정확한 고장진단 기술의 적용을 가능케 한다. 이를 위해 본 학위논문은 1) 풍력발전 운행 데이터의 분류 시스템 개발, 2) 진동기반 고장진단 기술 개발로 구성되어 있다. 첫 번째 연구에서는, 방대한 양의 풍력발전 데이터를 해당 풍력발전기의 거동 특성 (로터 회전 속도, 발전량)에 의거하여 유의미한 네 가지 (Class I. stationaryClass II. quasi-stationaryClass III. non-stationary with high correlationClass IV. non-stationary with no correlation) 클래스와 무의미한 한 가지 (Class V. idle) 클래스로 분류한다. 이후 각 클래스에 해당하는 데이터의 특성에 기반하여 최적의 고장진단 계획을 설계한다. 데이터 분류기법 개발을 위해 영흥 풍력단지로부터 취득한 풍력발전기의 거동 정보를 이용하였다. 두 번째 연구에서는 정의된 클래스 중 두 가지 클래스 (Class I & II)를 토대로 진동기반 고장진단 기술을 개발한다. 고장진단 기술은 보통 신호의 노이즈를 제거하기 위한 시간 동기 평균화 (Time synchronous averaging)과 유의미한 건전성 데이터를 추출하기 위한 오더분석으로 구성될 수 있다. 하지만 풍력발전기의 유성기어박스의 경우 내부에 포함되어 있는 여러 기어들의 복합적인 작용과 더불어 유성 기어의 축이 계속적으로 변하는 문제가 발생하기 때문에 기존의 고장진단 방법을 적용할 수 없다. 따라서 이 논문에서는 풍력발전기의 유성 기어박스에 대한 고장진단을 위해 새로운 시간 동기 평균화 방법인 자기상관함수 기반 시간동기 평균화 (Autocorrelation-based time synchronous averaging) 기법을 개발하였다. 제안된 진동기반 고장진단 기법을 검증하기 위해서 두 가지 신호(수학적 신호, 테스트베드로부터 취득한 신호)가 사용되었다. 이를 위해 우선 두 개의 모터와 메인 베어링, 플라이휠, 기어박스 그리고 13개의 센서 시스템이 구축되어 있는 2kW 풍력발전기 테스트베드가 설계되었다. 특히 인위적 고장이 인가된 기어가 기어박스에 조립될 수 있도록 설계되어 고장진단 연구에 활용할 수 있도록 하였다. 그리고 해당 테스트베드의 거동을 수학적 신호(analytical signal)로 표현하여 고장진단 기법을 사전 검증하였다. 정상 (healthy) 기어박스와 고장(faulty) 기어박스로부터 취득한 신호를 분석하기 위해 자기상관함수 기반 시간동기 평균기법과 오더 분석법을 사용한 결과 제안된 고장진단 기법은 정상 (healthy) 신호와 고장(faulty) 신호를 잘 구별할 수 있었다.Reliability of wind turbines (WT) is a challenging issue in wind energy industry. In particular, a gearbox in a WT has the highest risk because of its high maintenance cost. Despite many prior attempts to develop diagnostics techniques for WTs, one has faced many grand challenges including 1) inaccuracy in fault diagnostics due to random and non-stationary signals and 2) inefficiency in fault diagnostics with big sensory data (e.g. vibration) from many sensors in a WT. This study thus aims at developing a generic guideline and framework for gearbox fault diagnostics. This framework enables accurate diagnostic analysis while working with a massive volume of sensory data from many sensors in an efficient manner. This paper proposes two key ideas in the following research areas as: 1) classification of operational data, and 2) vibration-based fault diagnostics method. First, this study has classified the operation conditions into four non-trivial (Class I. stationaryClass IV. non-stationary with no correlation) conditions and one trivial (Class V. idle) condition in terms of the operation data (rotor speed, and power) of the WTs. Data classification has been conducted with real operational data acquired from Young Heung wind farms. Next, this study has also designed diagnostics methods for the first non-trivial class (Class I) based on the characteristics of the data classes. A core technique for the fault diagnostics is an order analysis method using Time Synchronous Averaging (TSA), where TSA is generally used for signal de-noising and the order analysis for the extraction of health data for a gearbox. It is, however, a daunting task to execute the fault diagnostics using the conventional TSA for a planetary gearbox because of multiple mesh contacts and rotation of the axes of planet gears. This paper proposes a new TSA idea, referred to as Autocorrelation-based TSA (ATSA) for the order analysis, particularly for a planetary gearbox. For the demonstration of the proposed diagnostics framework, two signals were employed: analytical signals and signals from a WT testbed. A 2kW WT testbed was designed with two DC motors, main bearing, flywheel and gearboxes with 13 sensors. A faulty gear was machined with different crack lengths at the root of the gear mesh and assembled into the gearbox. The order analysis based on ATSA processed the signals acquired from the healthy and faulty gearbox. It was concluded that the proposed diagnostics method can distinguish the faulty condition of the gearbox from the healthy one.Abstract i List of Tables vi List of Figures vii Nomenclatures xi Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Scope of research 3 1.3 Structure of the Thesis 4 Chapter 2. Review of Condition Monitoring 5 2.1 SCADA-based Condition Monitoring 5 2.2 Vibration-based Condition Monitoring System(CMS) 7 2.2.1 Spectral Analysis 7 2.2.2 Time-frequency analysis 8 Chapter 3. Classification of Operation Data 11 3.1 Introduction 11 3.2 Classification Method 12 3.3 Criterion for Quantitative Classification 13 3.4 Diagnostics Plans for the Classes 19 3.5 Results and Discussion 22 Chapter 4. Autocorrelation-Based Time Synchronous Averaging 24 4.1 Basic Concept of TSA 24 4.2 Overview of Planetary Gearbox 28 4.3 Conventional TSA for Planetary Gearbox Diagnostics 33 4.4 Autocorrelation-based TSA (ATSA) 38 4.5 Advantages of ATSA 44 Chapter 5. Health Data for WT Gearbox Diagnostics 46 5.1 Review of Health Data for Gearbox Diagnostics 46 5.1.1 GEN 47 5.1.2 RAW 49 5.1.3 TSA 49 5.1.4 RES 50 5.1.5 DIF 52 5.1.6 BPM 53 5.2 Procedures for Calculating Health Data of WT Gearbox 54 Chapter 6. Validation Study for ATSA 56 6.1 Design of Signal 56 6.1.1 Design of the Analytical Signal 56 6.1.2 Design of Testbed 58 6.1.3 Design of Experiment (DOE) 60 6.2 Results and Discussion 61 6.2.1 Analytical Signal 61 6.2.2 Testbed Signal 62 Chapter 7. Conclusion 67 7.1 Conclusion 67 7.2 Future Research 68 Bibliography 70 국문 초록 78 감사의 글 81Maste

Monitoring concept study for aerospace power gear box drive train

Using a gearbox in a turbojet engine implies additional monitoring tasks due to new introduced failure modes. This paper outlines monitoring options to address technical diagnosis of the world’s most powerful aerospace gearbox. For this novel technology different monitoring options are assessed to enable the trade between technical effort and monitoring capability. In this paper options to monitor the gears and journal bearings are described. To detect gear wear, pitting, and gear teeth cracks the use of acceleration, acoustic emission sensors, and different methods will be assessed. First stage results are based on Back2Back test run results in occurring pitting and gear teeth loss [1]. The journal bearing mixed friction will be detected by the use of an acoustic emission sensor [3], [5]. Due to the location of the journal bearing in the rotating area of the gearbox a Wireless Data Transfer Unit (WDTU) must be introduced [6], [7]. Results of early subscale component test runs are used to define requirements to adjust the WDTU and accommodate the new power gearbox (PGB) requirements. The electronics of the WDTU must cope with challenges such as the environmental conditions of the gearbox. To extract the mixed friction pattern by the applied signal processing steps from the noise disturbance caused by gear mesh is a technical challenge. Finally the paper closes with a recommendation on how to monitor such a gearbox and provides an outlook to the next test campaign, where the WDTU will be applied based on a back2back configuration of a subscale planetary gearbox [8]

Planetary Gearbox Fault Detection Using Vibration Separation Techniques

Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection

Internal vibration monitoring of a planetary gearbox

Vibration monitoring is widely used to determine the condition of various mechanical systems. Traditionally a transducer is attached to the structure under investigation and the vibration signal recorded. This signal is then processed and the required information extracted from the signal. With epicyclic gearboxes this traditional approach is not advisable. This is in part due to the fact that the planet gears rotate internally on a planet carrier. Special techniques are therefore required to extract a viable data signal from the measured vibration signal. These techniques require an additional post-processing step in which a compiled data signal is extracted from the measured data signal. This work investigates the possibility of mounting transducers internally on the rotating planet carrier. Mounting transducers at this location removes the relative motion seen in traditional measurement techniques. An epicyclic gearbox is modified to facilitate the internal mounting of the accelerometers. A number of implementation problems are highlighted and solutions to these problems are discussed. A large portion of the work is dedicated to implementing and qualifying the epicyclic time synchronous averaging technique which is traditionally used to evaluate epicyclic gearboxes. As this technique forms the basis to evaluate the data obtained from internal measurements, it is of fundamental importance that the technique is implemented correctly. It is shown that vibration data can be reliably measured internally, by means of accelerometers mounted on the planet carrier. The internally measured data is compared to data obtained by traditional techniques and shown to be equally adept in detecting deterioration of a planet gear tooth. Simple condition indicators were used to compare the vibration data of the two techniques. It was seen that the data obtained from the internally mounted accelerometers was equally, and in certain cases, slightly more sensitive to planet gear damage. This implies that the technique can be used successfully to evaluate epicyclic gearbox damage. There are a number of practical implementation problems that will limit the use of this technique. As the technology becomes available to transmit measured vibration signals wirelessly, the application of the internal measurement technique will become more viable. A preliminary investigation was also launched into the relationship between a planetary gearbox with a single planet gear and one with multiple planet gears. It is illustrated that vibration data, measured from a gearbox containing a single planet gear, shows an increased sensitivity to planet gear damage. Although a special test rig might be required, the increased sensitivity to damage can provide a method to test planet gears in critical applications such as aircraft gearboxes. CopyrightDissertation (MEng)--University of Pretoria, 2010.Mechanical and Aeronautical Engineeringunrestricte