Patent US7124637B2 successfully refreshed from Google Patents.
US7124637B2
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Determining amplitude limits for vibration spectra
Assignee: Tyco Fire and Security GmbH
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Filing: 2004-03-22
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Granted: 2006-10-24
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Expires: 2024-03-22
Abstract
Abstract A method for determining vibration amplitude limits of a mechanical device. The method comprises identifying a mechanical device and a frequency range for a spectrum to be analyzed, retrieving vibration spectra comprising individual spectrum for the mechanical device and the frequency range, calculating frequency for the individual spectrum, and identifying the individual spectrum with the smallest number of frequency lines. In addition, the method comprises calculating noise bandwidths and a largest noise bandwidth, removing outlier data, calculating conditional kernel density, and calculating vibration amplitude limits to detect faults in the mechanical device.
Inventors
- 👤 Ashish Singhal
- 👤 John E. Seem
- 👤 Mark J. Muench
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-27 19:01
CPC Classifications
G; G01; G01N; G01N29/00; G01N29/44; G01N29/46; G01N29/04; G01N29/11; G01N29/4472
Claims Analysis
Total Claims: 24
Claim 1
Independent Claim
66 words
A method for detecting faults in a chiller based on vibration amplitude limits, comprising: calculating vibration amplitude limits of the chiller using statistics and historical data for the chiller; estimating an at least two-dimensional density estimate; and weighting the historical data based on when the historical data was generated; wherein the vibration amplitude limits are calculated as a function of frequency for an entire frequency spectrum.
Claim 2
Independent Claim
66 words
A method for detecting faults in a chiller based on vibration amplitude limits, comprising: calculating vibration amplitude limits of the chiller using statistics and historical data for the chiller; estimating an at least two-dimensional density estimate; and weighting the historical data based on when the historical data was generated; wherein the vibration amplitude limits are calculated as a function of frequency for an entire frequency spectrum.
Claim 3
Dependent Claim
10 words
The method of claim 1 further comprising removing outlier data.
Claim 4
Dependent Claim
21 words
The method of claim 2 wherein the at least two-dimensional density estimate utilizes frequency and amplitude directions of the frequency spectrum.
Claim 5
Dependent Claim
18 words
The method of claim 3 wherein the at least two-dimensional density estimate is a d-dimensional kernel density estimate.
Claim 6
Dependent Claim
91 words
The method of claim 4 wherein the d-dimensional kernel density estimate for point x of a dataset with n data points is given by: p ( x ) = 1 n ∑ j = 1 n H - 1 / 2 K ( H - 1 / 2 ( x - x j ) ) where, x j is the j th observation of the dataset, K (u) is a d-dimensional kernel, H is a bandwidth matrix, and |•| denotes a matrix determinant.
Claim 7
Dependent Claim
19 words
The method of claim 4 further including obtaining vibration spectra comprising individual spectrum for the chiller from a database.
Claim 8
Dependent Claim
26 words
The method of claim 6 further comprising calculating a frequency for the individual spectrum and identifying an individual spectrum having the smallest number of frequency lines.
Claim 9
Dependent Claim
15 words
The method of claim 7 further comprising calculating noise bandwidths and a largest noise bandwidth.
Claim 10
Dependent Claim
18 words
The method of claim 8 further comprising collecting vibration data from all spectra in a given frequency range.
Claim 11
Dependent Claim
12 words
The method of claim 1 further comprising calculating a conditional kernel density.
Claim 12
Dependent Claim
21 words
The method of claim 10 wherein calculating the conditional kernel density comprises estimating an unknown probability density for a given dataset.
Claim 13
Independent Claim
84 words
A method for determining vibration amplitude limits of a mechanical device comprising: identifying a mechanical device and a frequency range for a spectrum to be analyzed; retrieving vibration spectra comprising individual spectrum for the mechanical device and the frequency range; calculating frequency for the individual spectrum; identifying the individual spectrum with a smallest number of frequency lines; calculating noise bandwidths and a largest noise bandwidth; removing outlier data; calculating conditional kernel density; and calculating vibration amplitude limits to detect faults in the mechanical device.
Claim 14
Independent Claim
84 words
A method for determining vibration amplitude limits of a mechanical device comprising: identifying a mechanical device and a frequency range for a spectrum to be analyzed; retrieving vibration spectra comprising individual spectrum for the mechanical device and the frequency range; calculating frequency for the individual spectrum; identifying the individual spectrum with a smallest number of frequency lines; calculating noise bandwidths and a largest noise bandwidth; removing outlier data; calculating conditional kernel density; and calculating vibration amplitude limits to detect faults in the mechanical device.
Claim 15
Dependent Claim
16 words
The method of claim 12 wherein the mechanical device comprises a chiller for an HVAC system.
Claim 16
Dependent Claim
22 words
The method of claim 12 wherein the vibration spectra for the mechanical device and the frequency range is obtained from a database.
Claim 17
Dependent Claim
20 words
The method of claim 14 wherein calculating conditional kernel density comprises estimating an unknown probability density for a given dataset.
Claim 18
Dependent Claim
101 words
The method of claim 15 wherein the probability density estimate at a point x for a one-dimensional dataset with n data points is given by: p ( x ) = 1 nh ∑ j = 1 n κ ( x - x j h ) where, x j is the j th observation of the dataset, h is a bandwidth that characterizes a spread of the kernel, and κ(•) is a kernel density function that is symmetric and satisfies the condition: ∫ - ∞ ∞ κ ( u ) ⅆ u = 1.
Claim 19
Dependent Claim
27 words
The method of claim 15 wherein the kernel density estimate is at least a two-dimensional kernel density estimate utilizing frequency and amplitude directions of the frequency spectrum.
Claim 20
Dependent Claim
71 words
The method of claim 17 wherein a d-dimensional kernel density estimate is given by: p ( x ) = 1 n ∑ j = 1 n H - 1 / 2 K ( H - 1 / 2 ( x - x j ) ) where K (u) is a d-dimensional kernel, H is a bandwidth matrix, and |•| denotes a matrix determinant.
Claim 21
Independent Claim
198 words
A method for determining vibration amplitude limits to detect faults in mechanical equipment, comprising: estimating a data probability distribution based on data for the mechanical equipment; utilizing the data probability distribution to calculate the vibration amplitude limits, such that the data probability distribution is calculated using statistics and historical data of the mechanical equipment including using a kernel density method, wherein the kernel density method comprises calculating conditional kernel density including estimating an unknown probability density for a given dataset, the probability density estimate at a point x for a one-dimensional dataset with n data points being given by: p ( x ) = 1 nh ∑ j = 1 n κ ( x - x j h ) where, x j is the j th observation of dataset X, h is a bandwidth that characterizes a spread of the kernel, and κ(•) is a kernel density function that is symmetric and satisfies the condition: ∫ - ∞ ∞ κ ( u ) ⅆ u = 1 ; removing outlier data; and calculating the vibration amplitude limits as a function of frequency for a substantial portion of the frequency spectrum.
Claim 22
Independent Claim
198 words
A method for determining vibration amplitude limits to detect faults in mechanical equipment, comprising: estimating a data probability distribution based on data for the mechanical equipment; utilizing the data probability distribution to calculate the vibration amplitude limits, such that the data probability distribution is calculated using statistics and historical data of the mechanical equipment including using a kernel density method, wherein the kernel density method comprises calculating conditional kernel density including estimating an unknown probability density for a given dataset, the probability density estimate at a point x for a one-dimensional dataset with n data points being given by: p ( x ) = 1 nh ∑ j = 1 n κ ( x - x j h ) where, x j is the j th observation of dataset X, h is a bandwidth that characterizes a spread of the kernel, and κ(•) is a kernel density function that is symmetric and satisfies the condition: ∫ - ∞ ∞ κ ( u ) ⅆ u = 1 ; removing outlier data; and calculating the vibration amplitude limits as a function of frequency for a substantial portion of the frequency spectrum.
Claim 23
Dependent Claim
25 words
The method of claim 19 wherein the kernel density estimate is a two-dimensional kernel density estimate utilizing frequency and amplitude directions of the frequency spectrum.
Claim 24
Dependent Claim
72 words
The method of claim 20 wherein a d-dimensional kernel density estimate is generally written as: p ( x ) = 1 n ∑ j = 1 n H - 1 / 2 K ( H - 1 / 2 ( x - x j ) ) where K (u) is a d-dimensional kernel, H is a bandwidth matrix, and |•| denotes a matrix determinant.
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