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Objective

To demonstrate the statistical robustness of SenQC’s ability to see defects with a high level of confidence.

To demonstrate SenQC’s abilities to “see” defects that cannot be detected by traditional methods.

Test Procedure

  1. Test was conducted on a inline 6 cylinder diesel engine inside regular engineering test facilities without any acoustic treatment.
  2. 6 SenQC probes were mounted next to each cylinder valve opening.
  3. Sound was measured from the engine.
  4. Shainin’s formulation was used to demonstrate the statistical robustness of SenQC.
  5. Conventional sound and vibration tests were used as benchmark.

Results

To guarantee bad products are unambiguously separated from the good ones, we modified Shainin’s formulation to consider the worst case scenario in QC testing as shown below:

where ΔM1 represents measurement errors of a product, ΔM2 the variations of a benchmark good product, and ΔP the smallest separation between bad and good products.

SenQC enables one to strip out background noise, thus reducing the measurement errors ΔM and variations in benchmark data. Consequently, the separation ratio is significantly increased.


Direct measured data vs. SenQC data

SenQC identified the defect that conventional method could not identify with high confidence.

Conclusions

SenQC is able to identify the defects in a test engine that conventional sound and vibration techniques cannot identify with high confidence.

SenQC is able to significantly enhance signal to noise ratios that traditional methods cannot.

SenQC is able to increase separations between bad and good products in a noisy environment.