Method and apparatus for analyzing an image to detect and...

Image analysis – Pattern recognition – Classification

Reexamination Certificate

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Details

C382S195000, C382S218000, C348S088000, C348S092000, C706S015000

Reexamination Certificate

active

06650779

ABSTRACT:

BACKGROUND OF THE INVENTION
1. Technical Field of the Invention
The present invention relates to a method and apparatus for detecting and classifying patterns and, amongst other things to a method and apparatus which utilizes multi-dimensional wavelet neural networks to detect and classify patterns.
2. Background Art
Current trends in industrial and manufacturing automation have placed an increased emphasis on the need for quality and reliability, both in the process control and product characterization areas. As the technologies are becoming more complicated, the production of virtually defect free products by reliable processes is becoming vital. Automatic control systems are becoming more complex as they are called upon to regulate critical dynamic systems and the associated control algorithms and control actuators entail a greater degree of sophistication. Consequently, there is a growing demand for fault tolerance, which can be achieved by improving the Fault Detection and Identification (FDI) concepts. FDI is of interest in a wide variety of applications such as control systems, image analysis, analysis of radar signals, smart sensors, texture analysis, medicine, industry, etc.
FDI algorithms generally consist of two portions, a detection portion and an classification portion. Detection is the process of deciding whether any one of a number of anticipated events, e.g. faults or defects, has occurred. Once the presence of an anticipated event has been established, classification distinguishes which particular anticipated event, e.g. defect, has occurred. There are a number of systems where traditional FDI techniques are not applicable due to the unavailability of analytic models. FDI becomes more difficult when there is a large variation in signal time constants. A high degree of system interdependencies, process and measurement noise, large-grain uncertainty and randomness make detection of anticipated events even more challenging.
Analysis of signals in either the time or frequency domain generally is not sufficient to capture faults that occur over a wide band of frequencies. Analysis of faults in pattern recognition applications should be localized in both the time and frequency domains for each input signal.
Over the last two decades, basic research in FDI has gained increased attention, mainly due to trends in automation, the need to address complex tasks, and the corresponding demand for higher availability and security of the control systems. However, a strong impetus has also come from the side of modem control theory that has brought forth powerful techniques in mathematical modeling, state estimation and parameter identification.
In general, FDI schemes can be classified broadly as: (1) model based FDI techniques; and (2) knowledge based FDI techniques. Model based techniques (analytic) generally use information about state variables from the model of the system to predict the future values. A disparity between the actual values and the predicted values suggests a possible fault. This is a very robust approach to FDI for systems where accurate models are available. However this approach has difficulty where accurate or complete models of the system are unavailable.
Model-based FDI techniques have been thoroughly tested and verified to perform satisfactorily in many applications. Based upon the methods of using the model, various approaches have been developed. For example, innovation-based techniques, such as Generalized Likelihood Ratio, are used for linear stochastic systems. This technique requires N+1 hypothesis testing: H
i
for the occurrence of fault i, i=1, . . . , N, and H
o
for no failure. The failure decision is based upon the maximum likelihood ratio of the conditional probabilities for H
i
and H
o
. A technique known as the Failure Sensitive Filters technique employs a class of filters wherein the primary criterion for the choice of the filter is that the effects of certain faults are accentuated in the filter residue. However, it is not always possible to design a filter that is sensitive only to a particular fault. Furthermore, a performance trade off is inherent in this method. For, as the sensitivity of the filter to new data is increased, by effectively increasing the bandwidth of the filter, the system becomes more sensitive to sensor noise and the performance of the detection algorithm in no-failure conditions degrades.
Another technique known as the Multiple Hypothesis Filter Detectors technique uses a bank of filters (one for each fault mode) and each filter is used to calculate the conditional probability that each failure mode has occurred. This technique generally is not very popular due to its level of complexity, which increases exponentially as the system expands. Since the complexity of the technique increases the processing time required, the processing time also increases exponentially with the complexity of the technique.
The Parity Space Approach exploits the inconsistency of data (due to failure) coming from different sources of the system. The Direct Redundancy or Hardware Redundancy technique uses the instantaneous values of different sensors while the Temporal Redundancy technique uses a dynamic relationship between sensor outputs and actuator inputs over a period of time. The Hardware Redundancy technique is simple and easy to apply. However, it requires multiple sensors for each variable. Another drawback of this technique is that it works on the assumption that only one sensor fails at a time (in a three sensor arrangement). Analytic Redundancy uses data from sensors representing different parameters of the system that can be mathematically related by the model or part of the model.
With the availability of mathematical and computational tools, the trend in FDI research has shifted toward analytical (i.e., functional) rather than physical redundancy. This implies that the inherent redundancy contained in the dynamic relationships among the system inputs and measured outputs is exploited for FDI. In such approaches, one makes use of a mathematical model of the system or models describing certain modules of the overall system.
The known techniques described above utilize a model of the system (or part of the system) for fault analysis. These techniques work satisfactorily as long as the model characteristics approximate the actual system. However, their performance degrades rapidly if the model does not accurately represent the actual system. Unfortunately, accurate models are not available for most systems. There is a growing potential for using knowledge-based models and algorithms instead of analytic ones. This approach is, of course, the only one available in cases where analytic models are not available. A comparison of a model-based technique and a knowledge-based technique is shown in FIG.
1
. It can be seen in
FIG. 1
that the knowledge base replaces the model in the overall architecture. This knowledge-based approach has created a new dimension of possible fault diagnosis techniques for complex processes with incomplete process knowledge. Whereas the analytic methods use quantitative analytical models, the expert systems approach makes use of qualitative models based on the available knowledge of the system. Although the intelligent FDI techniques do not require an accurate analytic model, they are restricted to identification of only predetermined defects. This is, however, acceptable in many cases as the fault modes in many applications are already known.
From the perspective of product characterization, one aspect of quality is perceived as a defect-free final product. Product inspection and defect classification is one of the key issues in the manufacturing arena, where defect classification is a pattern recognition problem. Manual inspection or traditional signal processing have proven to be inadequate in many applications. This is due to the presence of a high degree of uncertainty and complexity in these systems. Intelligent processing tools like fuzzy logic, neural networks and intelligent

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