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Novel Deep Learning Approach for Predictive Maintenance and Process Optimization
技术
- 分析与建模 - 机器学习
适用功能
- 维护
用例
- 预测性维护
服务
- 数据科学服务
挑战
大多数组织对其流程采用“反应性维护”方法，即在发生故障后对设备进行维修和更换。机器发生故障后的维修成本大约高出 10 倍，更不用说对收入和客户满意度的直接影响。通过“预防性维护”，设备按照预先设定的时间间隔进行维修或更换，以避免故障。虽然这种方法减少了计划外停机时间，但代价高昂，因为这些计划维修发生在设备没有任何问题的情况下。然而，预测性维护的好处是显着的，因此它正成为制造商的首选方法，使组织能够在需要时预见和安排维修和更换，实现设备 100% 的正常运行时间。制造业中传统机器学习的一个挑战是技术需要干净和完整的数据。然而，制造和过程数据可能是稀疏且嘈杂的。
目前，工程师很难访问和解释生产过程数据，他们依靠个人经验和意见来修改工艺参数。这会导致决策的不一致和潜在的次优决策，此外还增加了流程失败的风险，增加了相关的时间和成本。由于改变操作参数及其影响之间的固有时间滞后和惯性，生产线特别难以使用标准技术建模。通过应用相关和创新的深度学习技术来更有效地设计生产流程，也可以显着降低与废料和失败生产相关的成本。
解决方案
Intellegens 开发了一种机器学习工具 Alchemite，它可以在所有可用数据上训练模型，无论多么稀疏或嘈杂。它将所有可用数据汇集在一起，并使用潜在的相关性来准确预测缺失值并生成尽可能完整的模型。
该技术利用现有的流程数据来降低资产维护计划的成本，提高对整个系统的整体了解，优化制造中涉及的生产线和流程。
运营影响
相关案例.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
