Fault detection and diagnosis (FDD) system for end of production line testing of alternators

用于交流发电机生产线末端测试的故障检测和诊断 (FDD) 系统

• 批准号: 486107-2015
• 负责人: Habibi, Saeid
• 金额: $ 9.66万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618548
• 关键词: Fault; detection; diagnosis; FDD; system

Product quality and reliability continues to be one of the top priorities for auto manufacturers, and these mustbe maintained despite competitive pressures and the need for higher manufacturing efficiency and lowerproduction costs. As the Original Equipment Manufacturers (OEMs) move to leaner structures, the demandsplaced on their equipment suppliers to assure quality and reliability is increasing. Typically the automotivesuppliers have fewer internal resources than OEMs with less capability and expertise to perform the requiredquality and reliability analysis.This proposal is a continuation of a project with D&V Electronics on developing Fault Detection andDiagnosis (FDD) capabilities for end of production line testing of alternators. This cooperation has so farresulted in the development of an algorithm referred to as the Industrial Extended Multi-Scale PrincipleComponents Analysis (IEMSPCA) that has previously been applied to automotive starters. Both vibration andsound measurement were implemented in IEMSPCA. It performed FDD analysis in less than 15 seconds andhas achieved more than 96% detection success rate when applied to known faults on 26377 starter test cases.In this project the IEMSPCA will be further developed to be able to detect, diagnose, characterize and adapt tounknown as well as previously known fault conditions occurring during end-of-line testing of alternators.Intelligent strategies such as deep learning will be applied and a larger array of signals including vibration,sound, voltage, and current measurements will be used. Model-based strategies will be applied for improvingdiagnosis capabilities for a select range of fault conditions. The research outcomes will be implemented on aD&V Electronics test cell platform.

产品质量和可靠性仍然是汽车制造商的首要任务之一，尽管面临竞争压力，需要更高的制造效率和更低的生产成本，但必须保持这些。随着原始设备制造商（OEM）转向更精简的结构，对其设备供应商的要求也在增加，以确保质量和可靠性。通常情况下，汽车供应商比原始设备制造商拥有更少的内部资源，更少的能力和专业知识来执行所需的质量和可靠性分析。该提案是与D&V电子公司合作的一个项目的延续，该项目旨在开发故障检测和诊断（FDD）功能，用于交流发电机的生产线测试。这种合作迄今已导致被称为工业扩展多尺度主成分分析（IEMSPCA）的算法的发展，以前已被应用于汽车起动机。在IEMSPCA中实现了振动和噪声的测量。在26377个起动机测试案例中，IEMSPCA在不到15秒的时间内完成了FDD分析，并取得了96%以上的检测成功率。本项目将进一步开发IEMSPCA，使其能够检测、诊断、表征和适应在结束期间发生的未知以及先前已知的故障条件，交流发电机的线路测试。将应用深度学习等智能策略，并将使用更大的信号阵列，包括振动、声音、电压和电流测量。基于模型的战略将被应用于改进故障条件的选择范围的诊断能力。研究成果将在D &V电子测试单元平台上实现。