Web Based Artificial Intelligent Condition Monitoring System

工业中的状态监测(CM)识别机器性能的重大变化，这可能预示着正在发生的故障。然而，许多组织以某种形式使用CM，然而，根据《工厂服务杂志》的说法，当前的基于规则和基于案例的CM远不是最优的，因为由于以下方面的可变性，组合和分析传感器数据仍然存在问题：(1)工厂设备；(2)系统组件；(3)故障类型；(4)运行周期；(5)运行温度；(6)润滑；(7)和许多其他因素。随着数据处理能力变得越来越便宜，远程访问变得更加容易，开发下一代CM技术就有了机会。根据欧洲2020未来工厂报告，建模机器和过程模拟被认为是未来预测性维护的工具。人工智能与当前基于案例和规则的推理方法的应用和融合将允许设计出新的、更优越的CM解决方案。对最终用户组织的好处包括：(1)及时安排维护，提高设备可靠性；(2)主动采取措施预防和预测故障；(3)延长资产寿命；(4)减少停机时间，使每条生产线的产量增加3倍；以及(5)为55%的工业用户至少节省10%的能源。

Condition Monitoring (CM) in industry identifies significant changes in a machines performance which could be indicative of developing faults. Many organisations use CM in some form however, according to Plant Services Magazine, current ‘rule’ & ‘case’ based CM is far from optimal since it remains problematic to combine & analyse the array of sensor data due to the variability in (1) the plant equipment; (2) systems componentry; (3) types of failure; (4) operating cycles; (5) operating temperatures; (6) lubrication; (7) & many other factors. As data & processing power becomes ever cheaper and more accessible remotely, there is an opportunity in developing the ‘next generation’ of CM technologies. According to Europe 2020 Factories of the Future report, modelling machine & process simulation is deemed the future tool for predictive maintenance. The application & fusion of Artificial Intelligence with current case & rule-based reasoning methods would allow for new & far superior CM solutions to be engineered. Benefits to end-user organisations include: (1) timely scheduling of maintenance increasing equipment reliability; (2) proactively taking actions to prevent & predict failure; (3) increase asset lifespan; (4) reduced downtime leading to 3-fold increase in production per line; & (5) provide at least 10% energy savings for 55% of all industrial users.