산업용 로봇 고장 진단을 위한 암묵신호 분리 기반 다축 간섭 최소화 기법

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 윤병동.As smart factory is becoming popular, industrial robots are highly demanding in many manufacturing fields for factory automation. Unpredictable faults in the industrial robot could bring about interruptions in the whole manufacturing process. Therefore, many methods have been developed for fault detection of the industrial robots. Because gearboxes are the main parts in the power transmission system of industrial robots, fault detection of the gearboxes has been widely investigated. Especially, vibration analysis is a well-established technique for fault detection of the industrial robot gearbox. However, the vibration signals from the gearboxes are mixed convolutively and linearly at each axes, which makes it difficult to locate a damaged gearbox, and reduce fault detection performance. Thus, this paper develops a vibration signal separation technique for fault detection of industrial robot gearboxes under multi-axis interference. The developed method includes two steps, frequency domain independent component analysis (ICA-FD) and time domain independent component analysis (ICA-TD). ICA-FD is aimed at separating convolutive mixture of signals, and ICA-TD is aimed at eliminating the residual mixed components. The experiment is performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method could successfully separate the mixed signals by obtaining vibration signals from each gearbox, and enhance fault detection performance for the industrial robot gearboxes.Chapter 1. Introduction 1 1.1 Background and Motivation . 1 1.2 Scope of Research 1 1.3 Structure of the Thesis . 5 Chapter 2. Structure of Industrial Robot . 6 2.1 Structure of Experimental Robot 6 2.2 Problem in Industrial Robot Fault Detection . 8 Chapter 3. Methodology 10 3.1. Time Domain Independent Component Analysis (ICA-TD) . 10 3.2. Frequency Domain Independent Component Analysis (ICA-FD) 12 3.2.1 Separation 12 3.2.2 Permutation . 14 3.2.3 Scaling . 17 3.3. Multi-stage Independent Component Analysis (MSICA) . 17 Chapter 4. Experiment Evaluation . 19 4.1 Experiment with MSICA 19 4.1.1 Experiment Process . 19 4.1.2 Result Analysis 28 4.2 Comparison Experiment Using Basic ICA Method . 33 4.3 Comparison Experiment Using ICA-FD Method . 38 Chapter 5. Discussion and Conclusion . 45 5.1 Conclusions and Contributions 45 5.2 Future Work 46 Bibliography . 47Maste

Improved Slow Feature Analysis for Process Monitoring

Unsupervised multivariate statistical analysis models are valuable tools for process monitoring and fault diagnosis. Among them, slow feature analysis (SFA) is widely studied and used due to its explicit statistical properties, which aims to extract invariant features of temporally varying signals. This inclusion of dynamics in the model is important when working with process data where new samples are highly correlated to previous ones. However, the existing variations of SFA models cannot exploit increasingly tremendous data volume in modern industries, since they require the data to be fed in as a whole in the training stage. Further, sparsity is also desirable to provide interpretable models and prevent model overfitting. To address the aforementioned issues, a novel algorithm for inducing sparsity in SFA is first introduced, which is referred to as manifold sparse SFA (MSSFA). The non-smooth sparse SFA objective function is optimized using proximal gradient descent and the SFA constraint is fulfilled using manifold optimization. An associated fault detection and diagnosis framework is developed that retains the unsupervised nature of SFA. When compared to SFA, sparse SFA (SSFA), and sparse principal component analysis (SPCA), MSSFA shows superior performance in computational complexity, interpretability, fault detection, and fault diagnosis on the Tennessee Eastman process (TEP) and three-phase flow facility (TPFF) data sets. Furthermore, its sparsity is much improved over SFA and SSFA. Further, to exploit the increasing number of collected samples efficiently, a covariance free incremental SFA (IncSFA) is adapted in this work, which handles massive data efficiently and has a linear feature updating complexity with respect to data dimensionality. The IncSFA based process monitoring scheme is also proposed for anomaly detection. Further, a new incremental MSSFA (IncMSSFA) algorithm is also introduced that is able to use the same monitoring scheme. These two algorithms are compared against recursive SFA (RSFA) which can also process data incrementally. The efficiency of IncSFA-based monitoring is demonstrated with the TEP and TPFF data sets. The inclusion of sparsity in the IncMSSFA method provides superior monitoring performance at the cost of a quadratic complexity in terms of data dimensionality. This complexity is still an improvement over the cubic complexity of RSFA