Statistical Approaches for Fault Diagnostics and Root Cause Analysis with Industrial Applications

Department of Human and Systems EngineeringWith the advancement of sensors and data storage technology, condition-based maintenance (CBM) in manufacturing industries is becoming an appropriate approach to build a monitoring system. In this thesis, CBM is conducted for two manufacturing systems: multilayer ceramic capacitor (MLCC) stacker and power plant turbine system. A MLCC stacking machine is a core process of defining a quality of products. It is known that unparalleled upper and lower plates in a pressing step might cause MLCC misalignment. A machine health index which can represent status of this unevenness of the plates has been developed. To prove effectiveness of this machine health index, there have been several experiments and its validated algorithm is implemented in a real production system. Since a turbine system in power plants is core components, many diagnosis systems are already installed. Much information related to a power plant maintenance exists in a form of written documents, but these historical records are mostly not computerized. In addition, such information is often electronically stored as a string data format which is not appropriate data type for statistical analysis. Therefore, we propose to develop a knowledge-based expert system for a power plant monitoring system to overcome such limitations of computerization of scattered written information. Furthermore, an algorithm based on the recursive Bayesian estimation is suggested to recommend the most appropriate root cause from multiple observed symptoms of machine fault.ope

Wavelet-like convolutional neural network structure for time-series data classification

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models

Fault detection and identification method using observer-based residuals

Manufacturing machinery is becoming increasingly complicated, and machinery breakdowns not only reduce efficiency, but also pose safety hazards. Due to the needs for maintaining high reliability within facility operation, various methods for condition monitoring are suggested as the importance of maintenance has increased. Among the various prognostics and health management (PHM) techniques, this paper introduces a model-based fault detection and isolation (FDI) technique for the diagnosis of machine health conditions. The proposed approach identifies faults by extracting fault signal information such as the magnitude or shape of the fault based on a defined relationship between a fault signal and observer theory. To validate the proposed method, a numerical simulation is conducted to demonstrate its fault detection and identification capabilities in various situations. The proposed method and data-driven methods are then compared with regard to their fault diagnosis performance. (C) 2018 Elsevier Ltd. All rights reserved