Source: http://www.google.com/patents/US6456927?dq=7,346,545
Timestamp: 2015-02-01 07:40:19
Document Index: 686725997

Matched Legal Cases: ['art 341', 'art 325', 'art 325', 'art 325', 'art 341', 'art 325', 'art 325', 'art 325']

Patent US6456927 - Spectral knock detection method and system therefor - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsA spectral method, and corresponding system, for knock detection includes acquiring (603) spectral energy associated with vibration caused by a knocking condition from a running engine. Preferably, a sampled data system (105) acquires the spectral energy by converting an output from an accelerometer...http://www.google.com/patents/US6456927?utm_source=gb-gplus-sharePatent US6456927 - Spectral knock detection method and system thereforAdvanced Patent SearchPublication numberUS6456927 B1Publication typeGrantApplication numberUS 08/035,348Publication dateSep 24, 2002Filing dateMar 22, 1993Priority dateMar 22, 1993Fee statusLapsedPublication number035348, 08035348, US 6456927 B1, US 6456927B1, US-B1-6456927, US6456927 B1, US6456927B1InventorsDavid Frankowski, Neil J. Adams, Steven L. Plee, Donald J. Remboski, Jr.Original AssigneeMotorola, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (33), Non-Patent Citations (4), Referenced by (23), Classifications (14), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetSpectral knock detection method and system thereforUS 6456927 B1Abstract A spectral method, and corresponding system, for knock detection includes acquiring (603) spectral energy associated with vibration caused by a knocking condition from a running engine. Preferably, a sampled data system (105) acquires the spectral energy by converting an output from an accelerometer (101) into data samples (103) in a digital form. Then from the acquired spectral energy, a knock variable is derived from magnitudes of spectral components, representing a characteristic of a combustion chamber located within the running engine. In a preferred embodiment the knock variable is derived from magnitudes of spectral components related by ratios corresponding to Bessel function coefficients. The preferred embodiment includes a Digital Signal Processor (109) applying a Fast Fourier Transform method (503) to estimate a spectral content used to determine the knock variable. Then, a knock indication is provided (509) when the knock variable exceeds a magnitude of a predetermined threshold (507). Other embodiments include provision for providing a knock indication when a knock variable (703) derived from magnitudes of individual spectral components corresponding to characteristic knock spectra associated with acquired spectral energy exceeds a magnitude of a trended time weighted version of the knock variable by a predetermined magnitude (709).
FIELD OF THE INVENTION This invention is related to the field of knock detection relating to an internal combustion engine, and more specifically to a system for determining a knock condition while measuring spectral energy sensed by an engine coupled knock spectra responsive sensor.
BACKGROUND OF THE INVENTION Engine control systems with knock detection capability, are used to detect and eliminate knocking conditions, characteristic of the operation of internal combustion engines. Eliminating a knocking condition is important because, left unchecked engine power and efficiency will suffer, and combustion chamber and spark plugs will be damaged.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a system block diagram illustrating several high level functions, in accordance with the invention;
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS In the embodiments disclosed herein a spectral estimate of energy associated with vibration caused by a knocking condition sensed from a running engine provides a knock variable. A knock indication is provided when a magnitude of this knock variable exceeds a magnitude of a threshold.
This phenomena is both deterministic and repeatable. An acoustic model of a combustion chamber, represented by the following equation, may be used to model the anticipated knock phenomena related spectral behavior. f m , s = C  T π   D � x m , s EQUATION   1 where:
The resulting spectral energy fm,s is attributable to gas oscillations in the combustion chamber. This relationship is disclosed by C. S. Draper in his treatise �Pressure Waves Accompanying Detonation in the Internal Combustion Engine� published in the Journal of Aeronautical Science, Vol. 5, Number 6, pages 219-226, in April of 1938. Further information describing this known phenomena can be found in the following papers published by The Society of Automotive Engineers. SAE Technical Paper Series No. 871912, entitled �Detection of Higher Frequency Vibration to Improve Knock Controllability�, authored by Norihiko Nakamura, Eishi Ohno, Masanobu Kanamaru, and Tomoyuki Funayama, presented at a conference dated Oct. 19-22, 1987. And also, SAE Technical Paper Series No. 920808, entitled �Examination of Methods Used to Characterize Engine Knock�, authored by Paulius V. Puzinauskas, presented at a conference dated Feb. 24-28, 1992.
In the next step 505, a correlation of the spectral components corresponding to the relationship of the amplitudes representing the energy of particular frequency ranges in the respective bins 323 is done with the Bessel function related table 325 from FIG. 3. The spectrum with the highest correlation provides a knock variable, or estimate. This is done by aligning the first bin 329 of the Bessel function chart 341 with the 5,000 Hz bin located in the portion of bins 323 that represent the spectral data estimated by the FFT derived from the engine coupled knock spectra responsive sensor 101. Then, the next bin from the Bessel function related table 325 is aligned with a bin located in the portion of bins 323 corresponding to a frequency related to the first Bessel function ratio 1.66 from TABLE 1. In this case this frequency is 5,000 Hz�1.66 or 8,300 Hz. Then, the next bin from the Bessel function related table 325 is aligned with a bin located in the portion of bins 323 corresponding to a frequency related to the next Bessel function ratio 2.28 from TABLE 1. In this case this frequency is 5,000 Hz�2.28 or 11,400 Hz. This process is repeated for the remaining Bessel function related bins from chart 325�the last yielding alignment with a bin located in the portion of bins 323 corresponding to a frequency related to the last Bessel function ratio 3.81 from TABLE 1. In this case this frequency is 5,000 Hz�3.81 or 19,050 Hz. Then, amplitudes corresponding to the bins located aligned with the frequency biased chart 325 are summed. This summation is stored and the process repeated starting with the next bin higher than 5,000 Hz, until the highest summation, or correlation to the Bessel function biased chart 325 is found. Each of these stored variables represent the Bessel function related energy present at particular spectral locations.
To repeat the process the first bin 329 of the Bessel function chart 341 is aligned with the next, or 5,100 Hz bin located in the portion of bins 323 that represent the spectral data estimated by the FFT. Then, the next bin from the Bessel function related table 325 is aligned with a bin located in the portion of bins 323 corresponding to a frequency related to the first Bessel function ratio 1.66 from TABLE 1. In this case this frequency is 5,100 Hz 1.66 or 8,466 Hz. This process is repeated for the remaining Bessel function related bins from chart 325�the last yielding alignment with a bin located in the portion of bins 323 corresponding to a frequency related to the last Bessel function ratio 3.81 from TABLE 1. In this case this frequency is 5,100 Hz�3.81 or 19,431 Hz. Then, amplitudes corresponding to the bins located aligned with the frequency biased chart 325 are again summed. This summation is stored and the process repeated starting with the next bin higher than 5,100 Hz, until the highest summation, or correlation to the Bessel function biased chart 325 is found.
Conveniently, the frequencies selected to represent the non-knock variable are interdigitated with the characteristic knock frequencies. This assures a robust prediction of noise�thus improved knock detection because of the proximal location between the knock and non-knock frequencies. Some prior art systems measure noise broadband and in a separate spectral location distal from the knock spectra. In the case of the prior art approach various low and high frequency systemic noise sources could cause erroneous detection of a knocking condition. The precise location and bandwidth of the knock frequencies and non-knock frequencies is determined empirically. In this embodiment these relationships are disclosed in the following table.
Although the embodiments described herein teach the application of method steps on a hardware platform, a system may be easily constructed by those of ordinary skill in the art to emulate the method steps�thus yielding the same benefits taught in these embodiments.
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