An Efficient Alternative for Computing Algorithm Detection Thresholds

McDonald, Dennis L.

doi:10.4271/2006-01-0009

Cited by 3 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…0, showing that, the better the separation between the mean value a and the target value A t , the lower is the probability of knock occurrence. The standard deviation s and A t 2 a can be expressed in terms of the sample standard deviation s, using confidence intervals for the mean value and standard deviation, and equation (5). It can be shown that the upper bound of knock occurrence can be reduced via a reduction of the significance level a.…”

Section: Appendix Estimation Of the Probability Of Knock Occurrencementioning

confidence: 99%

“…These tests have been described by Picard [3]. Detection thresholds were presented using the confidence intervals in the paper by McDonald [5]. The detection threshold is adapted provided that the two-sample T test or the test for equal variances show the difference between newly estimated and precalibrated values of the parameters of the knock sensor signal at a certain frequency (average values and variances).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical engine knock modelling and adaptive control

Stotsky

2008

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

A new statistical concept of the knock control of a spark ignition automotive engine is proposed. The control aim is associated with the statistical hypothesis test which compares the threshold value with the average value of the maximum amplitude of the knock sensor signal at a certain frequency. Achievement of the control aim implies the desired separation between the average value of the maximum amplitude and the target value and hence the desired probability of knock occurrence. This new control concept allows the control algorithm parameters to be connected to the probability of knock occurrence and customer-related data. The regulation error is defined as the difference between the actual and the desired values of the statistic utilized. A control algorithm which is used for minimization of the regulation error realizes a simple count-up-count-down logic.A new adaptation algorithm for the knock detection threshold is developed. The confidence interval method is used as the basis for adaptation. A knock detection threshold is presented using a confidence interval with a certain significance level. The adaptation is performed for an aged engine so that the significance level is the same for the new and the aged engines despite the fact that the detection threshold values are different. This, in turn, guarantees the same knock detection performance for new and aged engines. A simple statistical model which includes generation of the amplitude signals, threshold value determination, and a knock sound model is developed for evaluation of the control concept. The statistical knock audibility concept is associated with the outlier detection method and is used in this paper for knock audibility judgement.A Volvo six-cylinder prototype engine equipped with cylinder pressure and block vibration sensors was used in the experiments. An external microphone was used for the knock sound measurements.

show abstract

Section: Appendix Estimation Of the Probability Of Knock Occurrencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical engine knock modelling and adaptive control

Stotsky

2008

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…A detection threshold determination method which uses confidence intervals is also described by McDonald [3].…”

Section: ð2þmentioning

confidence: 99%

Statistical algorithm for the adaptation of detection thresholds

Stotsky

2008

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

Many event detection mechanisms in spark ignition automotive engines are based on the comparison of the engine signals to the detection threshold values. Different signal qualities for new and aged engines necessitate the development of an adaptation algorithm for the detection thresholds. An adaptation algorithm for the detection threshold is developed with the confidence interval method used as the basis for adaptation. The detection threshold is presented using a confidence interval with a given significance level. The adaptation is performed for an aged engine such that the significance level remains constant regardless of engine age and changing detection threshold values. This, in turn, guarantees the same event detection performance for new and aged engines/sensors. Adaptation of the engine knock detection threshold is given as an example.

show abstract

A Real Time Statistical Method for Engine Knock Detection

2007

SAE Technical Paper Series

View full text Add to dashboard Cite

An Efficient Alternative for Computing Algorithm Detection Thresholds

Cited by 3 publications

References 1 publication

Statistical engine knock modelling and adaptive control

Statistical engine knock modelling and adaptive control

Statistical algorithm for the adaptation of detection thresholds

A Real Time Statistical Method for Engine Knock Detection

Contact Info

Product

Resources

About