Multiple Fault Diagnosis for Sheet Metal Fixtures Using Designated Component Analysis
نویسندگان
چکیده
This paper presents a new approach to multiple fault diagnosis for sheet metal fixtures using designated component analysis (DCA). DCA first defines a set of patterns based on product/process information, then finds the significance of these patterns from the measurement data and maps them to a particular set of faults. Existing diagnostics methods has been mainly developed for rigid-body-based 3-2-1 locating scheme. Here an N-2-1 locating scheme is considered since sheet metal parts are compliant. The proposed methodology integrates on-line measurement data, part geometry, fixture layout and sensor layout in detecting simultaneous multiple fixture faults. A diagnosability discussion for the different type of faults is presented. Finally, an application of the proposed method is presented through a computer simulation. @DOI: 10.1115/1.1643076#
منابع مشابه
Diagnosis of Multiple Fixture Faults in Machining Processes Using Designated Component Analysis
Fixture imperfections influence dimensional variation on machining processes. Therefore, a systematic method to isolate fixture faults will allow improving product quality. This paper presents a diagnosis methodology to identify root causes of fixture induce workpiece variation in machining. The proposed methodology is based on designated component analysis which extracts predefined variation p...
متن کاملFault detection and prognosis of assembly locating systems using piezoelectric transducers
Fixture faults have been identified as a principal root cause of defective products in assembly lines; however, there exists a lack of fast and accurate monitoring tools to detect fixture fault damage. Locating fixture damage causes a decrease in product quality and production throughput due to the extensive work required to detect and diagnosis a faulty fixture. In this paper, a unique algorit...
متن کاملA Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملA Fault Diagnosis Method for Automaton Based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کاملUsing PCA with LVQ, RBF, MLP, SOM and Continuous Wavelet Transform for Fault Diagnosis of Gearboxes
A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared...
متن کامل