Method of characterizing events in acquired waveform data from a metallic transmission cable

• US 6,195,614 B1
• Filed: 04/02/1998
• Issued: 02/27/2001
• Est. Priority Date: 06/02/1997
• Status: Expired due to Term

• Current Assignee: Focus Strategies Capital Advisors LLC
• Original Assignee: Tektronix Incorporated (Fortive Corp.)
• Inventors: Kochan, Phillip F.
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): Barbee, Manuel L.
• Application Number:
  US09/054,369
• Time in Patent Office: 1,062 Days
• Field of Search: 702/57-59, 702/65-71, 702/73, 702/74, 702/81, 702/79, 702/115-124, 702/126, 702/182, 702/183-185, 702/189, 702/193, 702/199, 324/76.2, 324/533, 324/534, 370/252
• US Class Current: 702/66
• CPC Class Codes: G01R 13/02 for displaying measured electric variables in digital form
• CPC Class Codes: G01R 31/11 using pulse reflection methods

Abstract

A method for characterizing events in acquired time domain reflectometry data from a metallic cable under test uses an edge or event detector for generating an array where positive array elements represent a positive leading edge of an event and negative array elements represent a negative leading edge of an event. Predominantly contiguous groups of positive and negative array elements are identified for defining regions, and elbow points and peak-valley points are determined for the regions having location and amplitude values corresponding to one of the waveform data points. The height for the event is determined as a function of the elbow point and the peak-valley point, and elbow point representing the location of the event and the event height are stored. The edge detector covers a defined number of waveform data points and moves over the waveform data point by point averaging the data within the detector, calculating an estimated noise characteristic and determining top and bottom threshold values at each data point. The thresholds are calculated as the estimated noise characteristic times a constant above the averaged waveform data within the detector. The elbow point is determined as the maximum deviation of the waveform data point from a line defined in relation to the peak-valley point and a point prior to the region in the waveform data.

Claims

1. A method of characterizing events in acquired waveform data from a metallic transmission cable wherein the waveform data is an array of data points acquired at selected pulsewidths for the interrogating pulses and receiver gain settings with each point having a location value and an amplitude value comprising the steps of:

a) generating a deviant array representative of an event in the acquired waveform data wherein positive array elements represent a positive leading edge of an event in the waveform data and negative array elements represent a negative leading edge of an event in the waveform data;

b) identifying a contiguous group of positive or negative array elements defining a region containing a single event, contiguous positive array elements defining a positive region and contiguous negative array elements defining a negative region;

c) determining an elbow point as a point of maximum bend at the beginning of the single event and a peak-valley point as the maximum amplitude of the single event for the region having location and amplitude values corresponding to one of the waveform data points;

d) determining a height for the event as a function of the elbow point and the peak-valley point; and

e) storing the elbow point representing the location of the event and the event height in an event table.

2. The method for characterizing events in acquired waveform data as recited in claim 1 wherein the generating step further comprises the steps of:

a) establishing a starting point in the waveform data for generating the deviant array;

b) defining a detector containing a defined number of waveform data points for generating the array elements;

c) averaging the waveform data within the detector;

d) calculating an estimated noise characteristic of the waveform data within the detector;

e) calculating a top threshold value as the estimated noise characteristic times a constant value above the averaged waveform data within the detector for comparing with waveform data point amplitude values for generating array elements;

f) calculating a bottom threshold value as the estimated noise characteristic times a constant value below the averaged waveform data within the detector for comparing with waveform data point amplitude values for generating array elements;

g) establishing a data point distance beyond the detector for comparing the threshold values with the waveform data point amplitude values;

h) establishing a detector advancement rate; and

i) advancing the detector over the waveform data points and repeating steps c, d, e and f of the generating step for each advancement of the detector.

3. The method for characterizing events in acquired waveform data as recited in claim 2 wherein the starting point establishing step further comprises the steps of:

a) determining the existence of an incident pulse in the acquired waveform data; and

b) moving the starting point within the waveform data by a distance equal to the pulsewidth of the incident pulse times a skip factor constant.

4. The method for characterizing events in acquired waveform data as recited in claim 2 wherein the noise characteristic calculating step further comprises the step of determining the mean absolute deviation of the waveform data within the detector.

5. The method for characterizing events in acquired waveform data as recited in claim 2 further comprising the steps of:

a) generating the positive array element when the waveform data point amplitude value is greater than the top threshold value for storing in the deviant array; and

b) generating a negative array element when the waveform data point amplitude value is less than the bottom threshold value for storing in the deviant array.

6. The method for characterizing events in acquired waveform data as recited in claim 1 wherein the determining step further comprises the steps of:

a) setting the largest waveform data point amplitude value over a distance starting at the beginning of the region and ending two region widths to the right of the peak-valley point;

b) defining a line segment having a first point whose location is prior to the beginning of the region and a distance equal to the larger of either a find elbow line factor constant times the region width or a detector halfwidth constant and a second point whose location is the left most of either the middle of the region or the peak-valley point; and

c) determining a point of maximum difference between waveform data point amplitude values within the line segment points and the line segment as the elbow point.

7. The method for characterizing events in acquired waveform data as recited in claim 1 further comprising the step of filtering out a region having a width smaller than a region width constant.

8. The method for characterizing events in acquired waveform data as recited in claim 1 further comprising the step of filtering out a region having an event height less than a height constant.

9. The method for characterizing events in acquired waveform data as recited in claim 1 further comprising the step of filtering out a region having an elbow point amplitude value that is greater than a saturation event constant.

10. The method for characterizing events in acquired waveform data as recited in claim 1 further comprising the step of calculating a return loss value for the event.

11. The method for characterizing events in acquired waveform data as recited in claim 10 further comprising the step of storing the return loss value for the event in the event table.

12. The method for characterizing events in acquired waveform data as recited in claim 1 further comprising the step of storing the receiver gain setting for the event and the pulsewidth of the interrogating pulses for the event in the event table.

13. The method for characterizing events in acquired waveform data as recited in claim 1 further comprising the step of repeating steps c, d and e for each region identified by step b.