Image analysis – Learning systems – Trainable classifiers or pattern recognizers
Reexamination Certificate
2000-03-06
2004-04-13
Werner, Brian (Department: 2721)
Image analysis
Learning systems
Trainable classifiers or pattern recognizers
C382S191000, C382S207000, C702S069000, C702S077000, C714S817000
Reexamination Certificate
active
06721445
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method for detecting anomalies in a digitized signal.
2. Discussion of the Related Art
In the context of industrial processings or during the operation of machines, it may be useful to monitor whether the operation flow is normal or abnormal. For example, in an industrial processing, several sensors will be placed at various points of a manufacturing line to store, for example, flow rates, pressures, temperatures . . . and follow their variations. In an automobile, a plane, a rocket, for example, sensors may be arranged on various propulsion elements or in the vicinity thereof to analyze operating characteristics thereof. Here again, the sensors may be sensors of the flow rate, temperature, pressure, speed, etc. Similarly, it may be desired to analyze the characteristics of a product under manufacturing, for example in the field of chemistry, pharmaceutics, agricultural-produce industry. The occurrence of possible structural anomalies in a building or a structure may also be monitored by vibration sensors. In all these cases, sensors will provide a continuous analysis of several manufacturing, operation, structure, or formulation parameters.
More generally, interest will be taken in detecting the presence of anomalies in any signal likely to fluctuate or in any computerized transmission of digitized data such as: passenger traffic, mobile telephony traffic indicators, etc.
In the state of the art, a known method for detecting anomalies in a signal consists of performing many preliminary tests, storing a large number of signals, analyzing the operation of the associated process, identifying signals including one or several anomalies corresponding to malfunctions of the process, and storing normal signals, which do not include these anomalies and correspond to a normal process operation. An iterative calculation based on an artificial neural network enables learning to discriminate a signal including anomalies from a normal signal. Abnormal signal phases can then be identified by using this experimental learning.
A major handicap of this method results from the fact that the implemented learning is very slow and requires performing a large number of tests and having a large number of examples of signals including the anomalies to be detected. Such a method can especially not be implemented to analyze anomalies and signals occurring relatively seldom, for example, to analyze the first instants characterizing the starting of a rocket or signals that only very exceptionally include anomalies, for example nuclear plant cooling circuit monitoring signals.
Another disadvantage of this method results from the fact that the calculation program of the neural network used will be specific to the analyzed signal and will not be applicable to detecting an anomaly on a signal of another nature. Thus, for each signal to be analyzed, a specific calculation program and a corresponding programming time will have to be provided.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a method for detecting anomalies overcoming the disadvantages of prior art methods.
A more specific object of the present invention is to provide such a method that is applicable to detecting anomalies in signals for which no previous sample of anomaly is available.
Another object of the present invention is to provide such a method in which anomalies of a signal can be discovered by an automatic unsupervised learning, without using previous tests on possible malfunctions of the process with which the signal is associated, and without requiring for the user to provide example or lists of possible anomalies.
Another object of the present invention is to provide such a method in which an initial learning of signal characteristics can be performed by an unsupervised automatic parameterizing to then detect anomalies by a statistical analysis or by comparison with memorized types of anomalies.
The present invention applies to a continuous signal as well as to an oscillating signal. This signal may originate in real time from a sensor recording of a system variable (temperature, pressure, vibration, noise, spectrographic analysis signal, x-ray analysis signal . . . ). The signal may also be a signal recorded by such a sensor stored in a digital databank. It may also be other types of signals stored in a digital databank, for example, as indicated, a signal characterizing the evolution of a data sequence representing any information, the variations of which are desired to be analyzed, for example data resulting from statistical analyses, from passenger or vehicle traffic indicators, etc.
Generally, according to the present invention, complex geometric characteristics, in frequency or time, of an initial signal portion are statistically analyzed to enable subsequently recognizing an anomaly on any subsequent portion of the same signal. This subsequent portion of the same signal may correspond to a separate sequence of a signal of same type. For example, if the taking-off of a rocket has been analyzed, information gathered upon analysis of a first rocket may be used for each of the considered signals to set the initial analysis parameters of the taking-off of the next rocket.
More specifically, to achieve the above-mentioned objects, the present invention provides a method for detecting anomalies in a digitized complex signal analyzed by a detection unit, including a machine learning step including a parameterizing of an automatic compression system, and a step of diagnosis of the intensity and/or the rarity of an anomaly,
the learning including the steps of:
1.1 selecting a succession of sequences of values of the analyzed signal corresponding to a succession of time windows (F
k
);
1.2 transforming the signal of each of the windows to extract therefrom characteristics of a type easily extracted by a human eye to form a first vector (D
k
) of dimension n; and
1.3 reducing number n of digital data by an automatic compression of the first vector (D
k
) to provide a second vector with coordinates substantially independent in probabilistic terms, of dimension p smaller than n;
the diagnosis including the steps of:
2.1 applying steps 1.1 to 1.3 to a polling window (F
k
) likely to include an anomaly;
2.2 comparing the obtained vector with a reference defined according to the same transformation and compression structure.
According to an embodiment of the present invention, the transformation intended for extracting signal characteristics associated with the human eye vision system is selected from the group including a fast Fourier transform (FFT), a transform on a Gabor-wavelet base, a maxima and/or minima extraction, and the like.
According to an embodiment of the present invention, the reference is an anomaly of predefined type of a signal such as a hump, a hiccup, a jolt, a trend change, a frequency shift or the like and an anomaly diagnosis signal is provided when there is a coincidence between the obtained vector and the reference.
According to an embodiment of the present invention, the reference results from a histogram of each coordinate of the second vector and an anomaly diagnosis is provided when a signal analyzed during a polling window deviates from said reference.
According to an embodiment of the present invention, applied to a digitized vibrating signal,
the learning includes the steps of:
3.1 selecting a succession of sequences of values of the analyzed signal corresponding to a succession of time windows (F
k
);
3.2 calculating, for each time window F
k
, a first vector (D
k
) of dimension n representing the spectral density of the analyzed signal; and
3.3 reducing number n of digital data by an automatic compression of the second vector (D
k
) to obtain a third spectral density vector with independent coordinates (ID
k
) and of dimension p smaller than n;
the diagnosis includes the steps of:
4.1 applying steps 3.1 to 3.3. to a polling window (F
k
) likely to include an anomaly.
According to an embodiment
McDermott & Will & Emery
Miriad Technologies
Werner Brian
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