A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings

A roller bearing fault diagnosis method based on EMD energy entropy and ANN

Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing

Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research

This paper presents a systematic study of various monitoring methods suitable,for automated monitoring of manufacturing processes. In general, monitoring is composed of two phases: learning and classification. In the learning phase, the key issue is to establish the relationship between monitoring indices (selected signature, features) and the process conditions. Based on this relationship and the current sensor signals, the process condition is then estimated in the classification phase. The monitoring methods discussed in this paper include pattern recognition, fuzzy systems, decision trees, expert systems and neural networks. A brief review of signal processing techniques commonly used in monitoring, such as statistical analysis, spectral analysis, system modeling, bi-spectral analysis and lime-,frequency distribution, is also included

Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods

This paper is the first attempt to implement a knowledge-based diagnostic approach for the auto-body assembly process launch. This approach enables quick detection and localization of assembly process faults based on in-line dimensional measurements. The proposed approach includes an auto-body assembly knowledge representation and a diagnostic reasoning mechanism. The knowledge representation is comprised of the product, tooling, process, and measurement representations in the form of hierarchical groups. The diagnostic reasoning performs fault diagnostic in three steps. First, an initial statistical analysis of measurement data is performed. Next, the Candidate Component and Candidate Station with the hypothetical fault are searched. Finally, the fault symptom is identified and the root cause is suggested. Two case studies are presented to demonstrate the implementation of the proposed method.

A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

A pattern recognition analysis scheme was developed for investigating vibration signals of bearings. Two normalized and dimensionless features are extracted by short-time signal processing techniques. Employing these two features, two linear discriminant functions have been established to detect defects on the outer race and rollers of bearings, respectively. Results of fault detection/diagnosis, based on the experimental data of imposed bearing defects, indicated the technique to be 14 percent better in the rate of success for the detection of defects than the best among the state-of-the-art

S. M. Wu

Papers

Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods

A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

On-Line Detection of Localized Defects in Bearings by Pattern Recognition Analysis

Monitoring Drilling Wear States by a Fuzzy Pattern Recognition Technique

Real-time CNC Tool Path Generation for Machining IGES Surfaces