Condition-Based Monitoring on High-Precision Gearbox for Robotic Applications

This work presents a theoretical and experimental study regarding defect detection in a robotic gearbox using vibration signals in both cyclostationary and noncyclostationary conditions. The existing work focuses on inferring the health of the robot during operation with little regard toward the defective element of the components. This article illustrates the detection of specific element damage of a robotic gearbox during a robotic cycle based on domain knowledge and presents a novel data-driven method for asset health. This starts by studying the robotic gearbox, specifically its kinematics as a planetary 2-stage reduction gearbox to acquire the knowledge of the rotations of each component. The signals acquired from a test bench with four sensors undergo different acquisition methods and signal processing techniques to correlate the elements' frequencies. The work shows the detection of the artificially created defects from the acquired vibration data, verifying the kinematic methodology and identifying the root cause of failure of such gearboxes. A novel resampling method, Binning, is presented and compared with the traditional signal processing techniques. Binning combined with Principal Component Analysis (PCA) as a data-driven method to infer the state of the gearbox is presented, tested, and validated. This work presents methods as a step toward automatized predictive maintenance on robots in industrial applications

잔차 합성곱 신경망을 통한 산업용 로봇 기어박스의 동작 적응형 퓨샷 고장 감지 방법

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 윤병동.Nowadays, industrial robots are indispensable equipment for automated manufacturing processes because they can perform repetitive tasks with consistent precision and accuracy. However, when faults occur in the industrial robot, it can lead to the unexpected shutdown of the production line, which brings significant economic losses, so the fault detection is important. The gearbox, one of the main drivetrain components of an industrial robot, is often subjected to high torque loads, and faults occur frequently. When faults occur in the gearbox, the amplitude and frequency of the torque signal are modulated, which leads to changes in the characteristics of the torque signal. Although several previous studies have proposed fault detection methods for industrial robots using torque signals, it is still a challenge to extract fault-related features under various environmental and operating conditions and to detect faults in the complex motions used in industrial sites To overcome such difficulties, in this paper, we propose a novel motion-adaptive few-shot (MAFS) fault detection method of industrial robot gearboxes using torque ripples via a one-dimensional (1D) residual-convolutional neural network (Res-CNN) and binary-supervised domain adaptation (BSDA). The overall procedure of the proposed method is as follows. First, applying the moving average filtering to the torque signal to extract the data trend, and the torque ripples of the high-frequency band are obtained as a residual value between the original signal and the filtered signal. Second, classifying the state of pre-processed torque ripples under various operating and environmental conditions. It is shown that Res-CNN network 1) distinguishes small differences between normal and fault torque ripples effectively, and 2) focuses on important regions of the input data by the attention effect. Third, after constructing the Siamese network with a pre-trained network in the source domain, which consisted of simple motions, detecting the faults on the target domain, which consisted of complex motions through BSDA. As a result, 1) the similarities of the jointly shared physical mechanisms of torque ripples between simple and complex motions are learned, and 2) faults of the gearbox are adaptively detected while the industrial robot executes complex motions. The proposed method showed the most superior accuracy over other deep learning-based methods in few-shot conditions where only one cycle of each normal and fault data of complex motions is available. In addition, the transferable regions on the torque ripples after domain adaptation was highlighted using 1D guided grad-CAM. The effectiveness of the proposed method was validated with experimental data of multi-axial welding motions in constant and transient speed, which are commonly executed in real-industrial fields such as the automobile manufacturing line. Furthermore, it is expected that the proposed method is applicable to other types of motions, such as inspection, painting, assembly, and so on. The source code is available on my GitHub page of https://github.com/oyt9306/MAFS.Chapter 1. Introduction 1 1.1 Research Motivation 1 1.2 Scope of Research 4 1.3 Thesis Layout 5 Chapter 2. Research Backgrounds 6 2.1 Interpretations of Torque Ripples 6 2.1.1. Causes of torque ripples 6 2.1.1. Modulations on torque ripples due to gearbox faults 8 2.2 Architectures of Res-CNN 11 2.2.1 Convolutional Operation 11 2.2.2 Pooling Operation 12 2.2.3 Activation 13 2.2.4 Batch Normalization 13 2.2.5 Residual Learning 15 2.3 Domain Adaptation (DA) 17 2.3.1 Few-shot domain adaptation 18 Chapter 3. Motion-Adaptive Few-Shot (MAFS) Fault Detection Method 20 3.1 Pre-processing 23 3.2 Network Pre-training 28 3.3 Binary-Supervised Domain Adaptation (BSDA) 31 Chapter 4. Experimental Validations 37 4.1 Experimental Settings 37 4.2 Pre-trained Network Generation 40 4.3 Motion-Adaptation with Few-Shot Learning 43 Chapter 5. Conclusion and Future Work 52 5.1 Conclusion 52 5.2 Contribution 52 5.3 Future Work 54 Bibliography 55 Appendix A. 1D Guided Grad-CAM 60 국문 초록 62Maste

A methodology and experimental implementation for industrial robot health assessment via torque signature analysis

This manuscript focuses on methodological and technological advances in the field of health assessment and predictive maintenance for industrial robots. We propose a non-intrusive methodology for industrial robot joint health assessment. Torque sensor data is used to create a digital signature given a defined trajectory and load combination. The signature of each individual robot is later used to diagnose mechanical deterioration. We prove the robustness and reliability of the methodology in a real industrial use case scenario. Then, an in depth mechanical inspection is carried out in order to identify the root cause of the failure diagnosed in this article. The proposed methodology is useful for medium and long term health assessment for industrial robots working in assembly lines, where years of almost uninterrupted work can cause irreversible damage

Condition Monitoring of a Belt-Based Transmission System for Comau Racer3 Robots

This project has been developed in collaboration with Comau Robotics S.p.a and the main goal is the development in China of an Health Monitoring Pro-cess using vibration analysis. This project is connected to the activity of Cost Reduction carried out by the PD Cost Engineering Department in China. The Project is divided in two part: 1. Data Acquisition 2. Data Analysis An Automatic Acquisition of the moni.log file is carried out and is discussed in Chapter 1. As for the Data Analysis is concerned a data driven approach is considered and developed in frequency domain through the FFT transform and in time domain using the Wavelet transform. In Chapter 2 a list of the techiques used nowadays for the Signal Analysis and the Vibration Monitoring is shown in time domain, frequency domain and time-frequency domain. In Chapter 3 the state of art of the Condition Monitoring of all the possible ma-chinery part is carried out from the evaluation of the spectrum of the current and speed. In Chapter 4 are evaluated disturbances that are not related to a fault but be-long to a normal behaviour of the system acting on the measured forces. Motor Torque Ripple and Output Noise Resolution are disturbance dependent on ve-locity and are mentioned in comparison to the one related to the configuration of the Robot. In Chapter 5 a particular study case is assigned: the noise problem due to belt-based power transmission system of the axis three of a Racer 3 Robot in Endu-rance test. The chapter presents the test plan done including all the simula-tions. In Chapter 6 all the results are shown demostrating how the vibration analysis carried out from an external sensor can be confirmed looking at the spectral content of the speed and the current. In the last Chapter the final conclusions and a possible development of this thesis are presented considering both a a Model of Signal and a Model Based approach

N-Link Modular Smart Robotic Arm

Today, 2.6 million industrial robots have been deployed for applications worldwide, allowing us to produce, sort, and create products faster. The goal of this MQP is to develop a modular robotic arm with N-links that can position its end effector in a 3D task space regarding of the number of links attached. It is also a priority that the arm is fault-tolerant and can be removed and attached in real-time without requiring a reboot. The results of this project show a detailed evaluation for the modular robotic arm that verifies that the arm would operate as predicted if it were fully constructed

University of Maryland walking robot: A design project for undergraduate students

The design and construction required that the walking robot machine be capable of completing a number of tasks including walking in a straight line, turning to change direction, and maneuvering over an obstable such as a set of stairs. The machine consists of two sets of four telescoping legs that alternately support the entire structure. A gear-box and crank-arm assembly is connected to the leg sets to provide the power required for the translational motion of the machine. By retracting all eight legs, the robot comes to rest on a central Bigfoot support. Turning is accomplished by rotating the machine about this support. The machine can be controlled by using either a user operated remote tether or the on-board computer for the execution of control commands. Absolute encoders are attached to all motors (leg, main drive, and Bigfoot) to provide the control computer with information regarding the status of the motors (up-down motion, forward or reverse rotation). Long and short range infrared sensors provide the computer with feedback information regarding the machine's relative position to a series of stripes and reflectors. These infrared sensors simulate how the robot might sense and gain information about the environment of Mars

특이 스펙트럼 템플릿 비교를 통한 전류 잔차를 이용한 산업용 로봇 기어박스 고장감지

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022.2. 윤병동.Industrial robots are essential equipment for process automation in a wide range of industrial fields. In manufacturing fields, unexpected faults of robots can severely damage the economy of a company. Fault can occur in various components of the robot and a faulty gearbox can have a significant effect on the robot's driving performance and manufactured product. Therefore, in this paper, gearbox fault detection of an industrial robot is performed using current signals applied to the actuating motor. The proposed method synchronizes normal current signal data to reference phase by resampling through Hilbert Transform. The synchronized signals are then split by singular value decomposition, and the principal components are extracted and averaged to establish normal template. Residual signal is then extracted by subtracting normal template from synchronized unknown signal. Finally, health management feature is calculated from the residual signal to perform fault detection. To quantify the performance of the proposed method, an evaluation metric ‘detection error’ is derived. The results of detection error show that the uncertainty of fault detection is declined through the proposed method. The distribution of health feature using proposed method is more concentrated than that of health feature using time synchronous averaging without the normal template.산업용 로봇은 넓은 범위의 산업 분야에서 필수적인 장비이다. 제조산업 분야에서, 예상못한 로봇의 고장은 회사에 심각한 경제적 타격을 초래할 수 있다. 고장은 로봇의 다양한 요소에서 일어날 수 있고, 고장난 기어박스는 로봇의 운행 성능과 제조된 물품의 품질에 중대한 영향을 미칠 수 있다. 그러므로 본 논문은 모터에 인가되는 전류 신호를 이용한 산업용 로봇의 기어박스 고장감지를 수행했다. 제안된 방법은 정상 전류 신호 데이터를 힐버트 변환을 통한 리샘플링을 통해 참조 위상에 동기화한다. 동기화된 신호들은 특이 스펙트럼 분석을 통해 분해되고, 주요 성분들이 추출되어 그 평균값이 정상 템플릿을 구축한다. 이후 잔차 신호가 고장 상태를 모르는 동기화된 신호에서 정상 템플릿을 뺌으로써 추출된다. 마지막으로 고장 감지를 위해 잔차 신호에서 건전성 인자가 계산된다. 제안된 방법의 성능을 정량화하기 위해 ‘감지 오차’라는 평가 지표를 도입했다. 이 지표를 통해 제안된 방법을 사용하였을 때 정상 템플릿을 사용하지 않은 시간 동기화 평균을 통해 구한 건전성 인자의 분포보다 밀집된 분포를 보였다. 즉, 감지 오차가 감소하여 고장 감지에서의 불확실성이 감소했다는 것을 확인할 수 있었다.Abstract i Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Dissertation Layout 3 Chapter 2 Theoretical Backgrounds 4 2.1 Characteristic of Current Signal in Fault Diagnosis of Industrial Robot 4 2.2 Hilbert Transform 5 2.3 Singular Spectrum Analysis 6 Chapter 3 Proposed Method 11 3.1 Signal Synchronizing 12 3.2 Establishing Normal Template & Fault Detection using Residual Signal 13 Chapter 4 Experimental Validation 15 4.1 Data Description 15 4.1.1 Testbed Setup 15 4.1.2 Acquired Data 17 4.2 Result & Discussion 18 Chapter 5 Conclusion & Future Work 22 References 23 국문 초록 25석