A Diagnosis Algorithms for a Rotary Indexing Machine
Rotary Indexing Machines (RIMs) are widely used in manufacturing due to their ability to perform multiple production steps on a single product without manual repositioning, reducing production time and improving accuracy and consistency. Despite their advantages, little research has been done on diagnosing faults in RIMs, especially from the perspective of the actual production steps carried out on these machines. Long downtimes due to failures are problematic, especially for smaller companies employing these machines. To address this gap, we propose a diagnosis algorithm based on the product perspective, which focuses on the product being processed by RIMs. The algorithm traces the steps that a product takes through the machine and is able to diagnose possible causes in case of failure. We also analyze the properties of RIMs and how these influence the diagnosis of faults in these machines. Our contributions are three-fold. Firstly, we provide an analysis of the properties of RIMs and how they influence the diagnosis of faults in these machines. Secondly, we suggest a diagnosis algorithm based on the product perspective capable of diagnosing faults in such a machine. Finally, we test this algorithm on a model of a rotary indexing machine, demonstrating its effectiveness in identifying faults and their root causes.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Fault Diagnosis of Rotary Machines using Deep Convolutional Neural Network with three axis signal input
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machine…
Deep LearningFault DiagnosisMachine learning-based method for linearization and error compensation of an absolute rotary encoder
The main objective of this work is to develop a miniaturized, high accuracy, single-turn absolute, rotary encoder called ASTRAS360. Its measurement principle is based on capturing an image that uniquely identifies the ro…
BIG-bench Machine LearningAn Intelligent Gearbox Fault Diagnosis under Different Operating Conditions using Adversarial Domain Adaptation
Effective gearbox diagnostic procedures can assist in rotary machinery's reliable and safe operation. On the other hand, the constant change in operational conditions, along with an absence of labeled data, have made fau…
DiagnosticDomain AdaptationFault DiagnosisUnsupervised Domain AdaptationCTkvr: KV Cache Retrieval for Long-Context LLMs via Centroid then Token Indexing
Large language models (LLMs) are increasingly applied in long-context scenarios such as multi-turn conversations. However, long contexts pose significant challenges for inference efficiency, including high memory overhea…
Eye Gaze Metrics and Analysis of AOI for Indexing Working Memory towards Predicting ADHD
ADHD is being recognized as a diagnosis which persists into adulthood impacting economic, occupational, and educational outcomes. There is an increased need to accurately diagnose and recommend interventions for this pop…
Diagnosticvalid