A Diagnosis Algorithms for a Rotary Indexing Machine

• URL: http://arxiv.org/abs/2305.15934v1
• Date: Thu, 25 May 2023 11:03:10 GMT
• Title: A Diagnosis Algorithms for a Rotary Indexing Machine
• Authors: Maria Krantz and Oliver Niggemann
• Abstract summary: 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.
• Score: 4.020523898765404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Data-driven Machinery Fault Detection: A Comprehensive Review [2.373572816573706]
  Timely and accurately identifying faulty machine signals is vital in industrial applications. Data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning approaches have been used ubiquitously in manufacturing. This survey provides a comprehensive review of the articles using different types of machine learning approaches for the detection and diagnosis of various types of machinery faults.
  arXiv  Detail & Related papers  (2024-05-29T07:50:47Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model [13.370180098472867]
  This paper proposes a health monitoring of movement disorder subject based on diamond stacked sparse autoencoder ensemble model (DsaeEM) This algorithm has two major components. First, feature expansion is designed using feature-embedded stacked sparse autoencoder (FSSAE) Second, a feature reduction mechanism is designed to remove the redundancy among the expanded features.
  arXiv  Detail & Related papers  (2023-03-15T11:34:06Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research [19.89641921844766]
  This paper implements a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. It identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines.
  arXiv  Detail & Related papers  (2022-05-30T14:54:27Z)
• Machine Learning Algorithms for Prediction of Penetration Depth and Geometrical Analysis of Weld in Friction Stir Spot Welding Process [0.0]
  The research work is based on the prediction of penetration depth using Supervised Machine Learning algorithms. A Friction Stir Spot Welding (FSSW) was used to join two elements of AA1230 aluminum alloys. The Robust Regression machine learning algorithm outperformed the rest of the algorithms by resulting in the coefficient of determination of 0.96.
  arXiv  Detail & Related papers  (2022-01-21T17:16:25Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery [2.055054374525828]
  This paper proposes a new approach for fault detection and diagnosis in rotating machinery. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults.
  arXiv  Detail & Related papers  (2021-02-23T18:28:18Z)
• Anytime Diagnosis for Reconfiguration [52.77024349608834]
  We introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain.
  arXiv  Detail & Related papers  (2021-02-19T11:45:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.