Abstract
Asset integrity and predictive maintenance models require field data for an accurate assessment of an asset’s condition. Historically these data collected periodically in the field by technicians using portable units. The significant investment in inexpensive microelectromechanical (MEMS) sensors mounted on untethered (energy-harvesting or battery-powered) microprocessors communicating wirelessly to the cloud is expected to change the way we collect asset health data. Permanently installed MEMS-based sensing units will enable near-real time data collection and reduce the safety exposure of technicians by eliminating the need to manually collect field data. With hundreds of MEMS-based sensing units expected to be installed at a single site it is vital to assure the data they produce and maintain them cost effectively. An asset management framework for validation of MEMS-based sensing units for condition monitoring and structural integrity (CM&SI) applications is proposed. An integral part of this framework is the proposed use of soft sensor models to replace technician inspections in the field. Soft sensor models are used in the process industry to stabilize product quality and process operations but there are few examples in asset management applications. The contributions of this paper are twofold. Firstly, we use an interdisciplinary approach drawing on electronics, process control, statistics, machine learning, and asset management fields to describe the emerging field of permanently installed MEMS-based sensing units for CM&SI. Secondly, we development a framework for assuring validation of the data these sensing units generate.
Highlights
There are safety and economic benefits if appropriate corrective action can be taken before asset failure occurs [1]
To move forward we propose the condition monitoring and structural integrity (CM&SI) community consider the experience the process industry has in developing soft sensor models for estimating difficult to measure process variables and product quality [19,20,21,22,23] and for validation of process sensors such as temperature, pressure and other process sensors [24,25,26,27]
The attraction of low cost MEMS-based sensing nodes connected wirelessly to a cloud platform is obvious for condition monitoring and structural health applications
Summary
There are safety and economic benefits if appropriate corrective action can be taken before asset failure occurs [1]. Condition monitoring and structural integrity (CM&SI) programs are integral to failure prevention Both are cyclic asset management processes involving program design, data collection and analysis, execution of maintenance recommendations and program review. Any move to have more permanently installed sensors necessitates the development of validation and maintenance programs for the sensing network. Motivated by this we propose the use of on-line mathematical models to identify out-of-calibration or malfunctioning sensing units. This will facilitate a move to an on-condition approach to managing the integrity of the permanently installed CM&SI sensing units. The objective of this paper is to present a framework for sensor validation management in CM&SI applications using permanently installed MEMS-based sensing units. We identify opportunities to improve sensor validation practice for the CM&SI community
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