技术
- 分析与建模 - 机器学习
- 功能应用 - 远程监控系统
- 基础设施即服务 (IaaS) - 其他
适用行业
- 金属
适用功能
- 维护
用例
- 机器状态监测
客户
中钢
关于客户
中钢集团(CSC)是台湾最大的综合钢铁制造商。
挑战
中钢集团(CSC)是台湾最大的综合钢铁制造商。我们的目标是提高设施维护和管理效率。首先,为了实现这一目标,我们知道我们需要了解机器状态监测 (MCM) 设备的设置成本与我们因意外故障而遭受的损失之间的权衡。我们还需要考虑我们拥有的关键设备以及维护它们的难度。有效的 MCM 可以帮助维护管理人员实时了解关键设备的状况,这是钢铁制造商的竞争优势。 1998年,我们第一台自主研发的设备在线监测诊断系统(FOMOS)安装在CSC生产线上。该系统在监测磨机振动等某些问题时表现良好,但几年后系统变得不那么可靠。我们发现硬件和软件的功能有限。我们知道我们可以构建一个更智能、连接性更强的系统,该系统可以利用处理能力、无线网络和软件连接方面的最新进展。我们意识到开发 MCM 系统相当容易,但很难解决我们面临的具体挑战。我们要解决的最大挑战是如何在不产生大量误报的情况下及早发现设备异常。由于振动是一种相对状态指标,受系统或结构动态刚度和传递率的影响很大,在实际情况中,经常会发现两台相同的设备在相似的位置和运行条件下运行几年后会出现不同的振动水平.通过使用不同的信号处理和算法,振动信号可以成为磨损、不平衡、未对准、冲击载荷和轴承故障等信息的关键指标。传统的监控和诊断系统使用相同或很少的指标和标准来监控和诊断各种机器状况,而不管机器操作的性质如何。因此,这些系统容易出现误报或漏报。更糟糕的是,由于监控设备种类繁多,操作制度复杂,并且在综合钢厂众多工艺线中的广泛应用,仅靠少数诊断专家实现我们的目标是不切实际的。因此,我们发现提高 MCM 系统有效性和效率的最佳方法是引入一些人工智能方法。
解决方案
开发智能的、面向问题的分析系统,利用物联网(IoT)对设施进行远程实时监控,将大量数据快速转换为设施管理,并立即发现设施异常。该系统还必须提高整体设施效率、节约能源、减少碳排放并增强工作场所的安全性。我们新的在线 MCM 系统 FOMOS-AI 是一种基于设备行为而非传统方法的专利自学习系统。为了从振动信号中提取有用的状态指标,我们将被监测设备的运行环境分为四种不同的模式,包括恒速和稳定负载、恒速和可变负载、变速和负载以及往复运动。为了获得更好的自诊断精度,每个模块通过同时获取多个交互状态信号来整合数据。此外,如果设施遇到特定故障,分析模块可以提高系统智能和故障识别能力。因此,它可以获取大量数据并自动将其转换为可操作的信息,用于状态监测和预测技术。我们在设计 FOMOS-AI 的硬件和软件架构时都考虑到了最高的灵活性,因此可以实现各种故障诊断和检测算法。 LabVIEW编程的系统软件由诊断中心、邮件设置和基线设置组成
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