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 Model for Robot Arm Pattern Identification using K-Means Clustering and Multi-Layer Perceptron

Predictive maintenance of industrial machines is one of the challenging applications in Industry 4.0. This paper presents a comprehensive methodology to identify robot arm (SCARA) movement patterns to detect the mechanical aging of the robot, which is determined by the abnormal movement of the robot arm. The dataset used is two robot arm movements that go from point A to B and then back to point A. Accelerometer data is used to measure the signal of SCARA actions, mainly focus on the non-linear movement. The identification of the movement pattern of the robot arm is made by combining k-means and multilayer perceptron. The proposed approach first extracts valuable features as characteristics of the two datasets from the time domain statistical value parameters. K-means clustering technique is initiated to label the training dataset. In this phase, the elbow curve is used to determine the number of clusters in the dataset, which is 2 clusters. Moreover, the assumption is used to determine which cluster is labeled as a normal and abnormal movement.  Hence, a multilayer perceptron approach is proposed to predict the testing dataset. The proposed multilayer perceptron model yields an accuracy of 94.14%, whereas its cross-validation yields an accuracy of 96.12%

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

open access articleAutonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%

Exploiting gan as an oversampling method for imbalanced data augmentation with application to the fault diagnosis of an industrial robot

O diagnóstico inteligente de falhas baseado em aprendizagem máquina geralmente requer um conjunto de dados balanceados para produzir um desempenho aceitável. No entanto, a obtenção de dados quando o equipamento industrial funciona com falhas é uma tarefa desafiante, resultando frequentemente num desequilíbrio entre dados obtidos em condições nominais e com falhas. As técnicas de aumento de dados são das abordagens mais promissoras para mitigar este problema. Redes adversárias generativas (GAN) são um tipo de modelo generativo que consiste de um módulo gerador e de um discriminador. Por meio de aprendizagem adversária entre estes módulos, o gerador otimizado pode produzir padrões sintéticos que podem ser usados para amumento de dados. Investigamos se asGANpodem ser usadas como uma ferramenta de sobre amostra- -gem para compensar um conjunto de dados desequilibrado em uma tarefa de diagnóstico de falhas num manipulador robótico industrial. Realizaram-se uma série de experiências para validar a viabilidade desta abordagem. A abordagem é comparada com seis cenários, incluindo o método clássico de sobre amostragem SMOTE. Os resultados mostram que a GAN supera todos os cenários comparados. Para mitigar dois problemas reconhecidos no treino das GAN, ou seja, instabilidade de treino e colapso de modo, é proposto o seguinte. Propomos uma generalização da GAN de erro quadrado médio (MSE GAN) da Wasserstein GAN com penalidade de gradiente (WGAN-GP), referida como VGAN (GAN baseado numa matriz V) para mitigar a instabilidade de treino. Além disso, propomos um novo critério para rastrear o modelo mais adequado durante o treino. Experiências com o MNIST e no conjunto de dados do manipulador robótico industrial mostram que o VGAN proposto supera outros modelos competitivos. A rede adversária generativa com consistência de ciclo (CycleGAN) visa lidar com o colapso de modo, uma condição em que o gerador produz pouca ou nenhuma variabilidade. Investigamos a distância fatiada de Wasserstein (SWD) na CycleGAN. O SWD é avaliado tanto no CycleGAN incondicional quanto no CycleGAN condicional com e sem mecanismos de compressão e excitação. Mais uma vez, dois conjuntos de dados são avaliados, ou seja, o MNIST e o conjunto de dados do manipulador robótico industrial. Os resultados mostram que o SWD tem menor custo computacional e supera o CycleGAN convencional.Machine learning based intelligent fault diagnosis often requires a balanced data set for yielding an acceptable performance. However, obtaining faulty data from industrial equipment is challenging, often resulting in an imbalance between data acquired in normal conditions and data acquired in the presence of faults. Data augmentation techniques are among the most promising approaches to mitigate such issue. Generative adversarial networks (GAN) are a type of generative model consisting of a generator module and a discriminator. Through adversarial learning between these modules, the optimised generator can produce synthetic patterns that can be used for data augmentation. We investigate whether GAN can be used as an oversampling tool to compensate for an imbalanced data set in an industrial robot fault diagnosis task. A series of experiments are performed to validate the feasibility of this approach. The approach is compared with six scenarios, including the classical oversampling method (SMOTE). Results show that GAN outperforms all the compared scenarios. To mitigate two recognised issues in GAN training, i.e., instability and mode collapse, the following is proposed. We proposed a generalization of both mean sqaure error (MSE GAN) and Wasserstein GAN with gradient penalty (WGAN-GP), referred to as VGAN (the V-matrix based GAN) to mitigate training instability. Also, a novel criterion is proposed to keep track of the most suitable model during training. Experiments on both the MNIST and the industrial robot data set show that the proposed VGAN outperforms other competitive models. Cycle consistency generative adversarial network (CycleGAN) is aiming at dealing with mode collapse, a condition where the generator yields little to none variability. We investigate the sliced Wasserstein distance (SWD) for CycleGAN. SWD is evaluated in both the unconditional CycleGAN and the conditional CycleGAN with and without squeeze-and-excitation mechanisms. Again, two data sets are evaluated, i.e., the MNIST and the industrial robot data set. Results show that SWD has less computational cost and outperforms conventional CycleGAN

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor