Accurate predictive maintenance based on four parameters

该项目旨在验证基于四个参数的预测性维护的假设。北方魁北克地区的经济高度依赖铁和铝的生产和转化。相关的工业活动确保了大部分人口的就业。传统的维护已不足以确保这些公司的竞争力。优化的资产管理和维护对于降低生产成本和机器停机时间至关重要。该项目将导致高精度维护方法的开发，改善3D建模和模拟方面的培训，并建立预测性维护方面的专业知识。开发的模型将在两个试验台上进行测试和改进;一个在Cégep de Sept Joules industrial，另一个在Metal 7使用的典型输送机上。对于不同的轴承和轴，将对两个工作台进行应力、剪切和扭转以及疲劳和数值模拟。模拟将针对不同的扭矩、转速和质量偏移情况运行。与此同时，将分析与这些机器元件故障频率相关的统计数据，并根据机器工作场景推断故障趋势。同时，使用制造商的规范（如果可用），将建立与机器操作场景相关的类似故障趋势。最后，试验台将配备加速度计、热传感器和对准传感器，以便真实的跟踪机器元件随时间和机器工作情况的故障。其思路是根据每个测量参数（数值模拟、统计数据、制造商的规格和仪器数据）并针对不同的机床

This project is aimed at validating the assumption of predictive maintenance based on four parameters. The economy of the Northern Quebec region is highly dependent on iron and aluminium production and transformation. Related industrial activities ensure employment for a large proportion of the population. Traditional maintenance is no longer enough to ensure competitiveness of these companies. Optimised asset management and maintenance is essential to reduce production cost and machine downtime. This project will lead to the development of a high precision maintenance method, improve training in 3D modelling and simulation, and establish a pole of expertise in predictive maintenance. The developed model will be tested and refined on two testbeds; one at Cégep de Sept Îles industrial and on a typical conveyor used at Metal7. For different bearings and shafts, the stress, shear and torsion as well as fatigue and numerical simulation will be performed for both benches. The simulations will be run for different torques, rotational speeds and mass offset scenarios. In parallel, statistical data related to frequency of failure of these machine elements will be analysed and information inferred as to failure tendencies according to machine working scenarios. At the same time, using manufacturer's specifications, when available, similar failure trends with respect to machine operating scenario will be established. Finally, the testbeds will be instrumented with accelerometers, heat sensors, and alignment sensors in order to follow in real time, failure of the machine elements with respect to time and machine working scenario. The idea is to establish according to each measurement parameter (numerical simulation, statistical data, manufacturers' specifications and instrumentation data) and for different machine