White Papers

Automatic Sensor and Wiring Detection in Data Acquisition Systems

July 26, 2016 | BY: Thomas Melia

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Reliable Data Acquisition

Good data is vital to the success of many airborne applications. Modern data acquisition systems allow for reliable, high fidelity data capture but this is contingent upon there being no wiring or sensor faults. When undetected faults are present it can render the collected data useless, a problem for applications such as flight test and structural health monitoring (SHM).

Junk data may result in expensive repeated flights or misleading information on the health of an aircraft. Built-in test functions within data acquisition units help to ensure that electronics faults are detected and reported to analysts. However, at the level of sensors and wiring there is no built-in test functionality of significance. Various problematic strategies are used to overcome this weak-point: using long life sensors, manual data quality checks and rigorous wiring testing etc. In this white paper we will discuss how machine learning algorithms can be used to detect common sensor and wiring faults, and introduce sensor level built-in test functionality.


Rubbish data in, rubbish data out

The aircraft instrumentation systems used to collect data often must endure high environmental stresses during flight. These are designed to be rugged and are qualified under various standards(e.g. MIL-STD-810, DO-160) and will often remain in the field for many months or even years gathering data. Each instrumentation system captures most of its data from remotely located sensors, connected to the acquisition units via wiring of variable quality.

Overall success for an aerospace instrumentation is affected dramatically when remotely connected sensors and/or wiring fail during operation. Typically, a complete failure will be obvious and discovered quickly, but more subtle faults may go undetected for long periods of time resulting in unreliable data.

Download our Automatic Sensor and Wiring Detection in Data Acquisition Systems White Paper to read more about:

  • Machine-learning
  • Sensor faults
  • Self-diagnostics
  • Instrumentation
  • Built-in-test


Author’s Biography

Thomas Melia

Product Manager (Aircraft Equipment)

Thomas Melia has 10 years of experience developing solutions for customers in many application areas (Audio Separation & Localization Algorithms, Software Defined Radios, Concrete Embedded RFIDs, Oil-Well Imaging ASICs, FSK/ASK Transceivers, Digital TV Tuners, Airborne Instrumentation). He studied at University College, Dublin obtaining an Electronic Engineering Degree in 2003 and a PhD in Digital Signal Processing in 2007. Since joining Curtiss-Wright in 2011, he has been responsible for bid management and the expansion of Continued Airworthiness and Avionics COTS offerings.

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