预测性维护—数字孪生

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本文摘要：（由ai生成）

预测性维护在航空业中通过数据分析和AI预测飞机潜在故障，帮助航空公司主动规划维修。关键发展点包括选适合的传感器、定制系统和数据物理解释。法国VibraTec集团是机械声学振动专家，提供创新解决方案增强产品可靠性。懿朵科技作为VibraTec的中国合作伙伴，提供振动噪声控制技术，服务于轨交、汽车、航空航天等，包括整车优化、软件开发、减振降噪产品设计、故障诊断与健康管理等服务。

Context & Issue

Predictive maintenance in aviation is an approach that uses data analysis and artificial intelligence to predict potential failures of aircraft equipment and systems before they occur. This enables airlines and aircraft operators to plan repairs and maintenance proactively, rather than reacting after failures occur.

Key Development Points

The right sensor in the right place

Extensive sensor database for measuring various physical parameters: acceleration, displacement, pressure, acoustics, rotation speed, deformation, temperature, current, etc.
Analog and digital sensors (MEMS)
On-board or ground instrumentation (endurance or performance benches).

From on-board sensor to virtual sensor via the digital twin

On-board instrumentation (anemometer, altitude, temperature, incidence, yaw)
Test-specific instrumentation: pressures, gauges, anemometer
Load characterization
Response characterization: virtual sensor = maximum stress

Difficulty: sensors should preferably be integrated at the design stage. For example, characterization of a dynamic transfer between a bearing defect (spalling) and an acceleration signal detected by the sensor, or a crack at the root of a gear tooth.

A system tailored to your needs

Hardware selection based on:
Host site: on-board or ground-based,
Type of storage or transfer,
Conditioners required for sensors,
Sampling frequency,
in-situ processing possibilities,
Number of measurement channels,
Type of processing required.
Possibility of working with hardware already installed.
Recording triggered by operating parameters, incidents or continuously.

A physical interpretation of the information provided by dynamic static signals is required

The amount of data to be analyzed is very large. A step of information contraction is necessary. The physical interpretation of the indicators enables us to target/select those that are relevant to the analysis of an organ’s state of health.

Finally, a Principal Component Analysis (PCA) is used to reduce the data. The synthesis of the information provided by all the selected indicators can be represented in the form of a global indicator.

Results & Benefits

Following detection of an anomaly, calculation of residual service life

Feedback: field feedback / endurance bench testing of components
Simulation: damage calculation / digital twin (sensor signal/damage transfer)

Decision-making

Validation of whether or not to replace the component during the next maintenance campaign
Planning of an urgent maintenance task to replace the faulty component

法国VibraTec集团，成立于1986年，拥有超过 37 年的经验，是复杂机械、声学和振动现象方面的专家。作为国际知名的咨询公司，Vibratec致力于研究开发务实和创新的解决方案，使产品、基础设施和设备更加可靠、耐用和安静。

懿朵科技，总部位于上海，作为法国VibraTec集团在中国区独家合作伙伴，是以振动与噪声控制为核心的高新科技企业。凭借多年行业经验及算法积累，懿朵科技为轨道交通、汽车行业、航空航天等领域提供整车及零部件优化、软件开发、减振降噪产品设计、故障诊断与健康管理、环境振动噪声测试与预测、专业工具/软件销售等服务。在追求卓越的道路上不断创新，为客户提供技术支持与解决方案。

来源：懿朵科技