Patent Application
20230139081
The present invention relates to systems and methods for analyzing and detecting connection anomalies in cable-based systems, and particularly but not exclusively to systems and methods for predicting failure of a linking component.
Cable or connection reliability and performance is an important and persistent problem in today's large, interconnected society. A significant amount of time and money is spent on analyzing and more importantly, detecting, connection anomalies. Often, it is helpful to discover connection anomalies before they occur or manifest into larger failures affecting critical systems. For example, with time, physical network links suffer from age related degradation resulting in connection instability and performance bottlenecks, reducing bandwidth and increasing packet loss. Perhaps even more problematic is a situation in which an extrinsic force destroys a cable link altogether. For example a submarine communications cable may be snagged by a fishing boat anchor, resulting in a total loss of the link between the connected entities.
According to embodiments of the invention, there is provided a system and method for cable anomaly detection. Embodiments of the invention may include: collecting, by a processor, a first set of cable measurement data; based on the first set of cable measurement data, creating a model which includes one or more groups; collecting a second set of cable measurement data, and for cable measurement data of the second set of cable measurement data, determining a probability of anomaly, wherein the probability of anomaly is based on the deviation of the cable measurement data of the second set of cable measurement data from the one or more groups of the model. Displaying the probability of anomaly for the cable measurement data of the second set of cable measurement data.
According to embodiments of the invention, the model may be a Gaussian mixture model.
Embodiments of the invention may include calculating a deviation, wherein a deviation may be the highest probability of the cable measurement data being part of the one or more groups of the model.
According to embodiments of the invention, there is provided a system and method for cable degradation detection. Embodiments of the invention may include collecting, by a processor, multiple types of cable measurement values. Based on the first set of cable measurement values, creating a model including one or more thresholds. The processor may be to collect a second set of new cable measurement values; and for a new cable measurement value, determining the probability that the cable measurement values follows the distribution of one or more groups of the model; and displaying an alert corresponding to the group associated with the highest probability.
Persons skilled in the art will thus appreciate the need to predict and detect cable or connection failure or anomalies in advance, significantly improving cable link reliability and link performance.
The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings in which:
It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn accurately or to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity, or several physical components may be included in one functional block or element. Reference numerals may be repeated among the figures to indicate corresponding or analogous elements.
In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components, modules, units and/or circuits have not been described in detail so as not to obscure the invention. For the sake of clarity, discussion of same or similar features or elements may not be repeated.
Although embodiments of the invention are not limited in this regard, discussions utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” “establishing”, “analyzing”, “checking”, or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulates and/or transforms data represented as physical (e.g., electronic) quantities within the computer's registers and/or memories into other data similarly represented as physical quantities within the computer's registers and/or memories or other information non-transitory storage medium that may store instructions to perform operations and/or processes. Although embodiments of the invention are not limited in this regard, the terms “plurality” and “a plurality” as used herein may include, for example, “multiple” or “two or more”. The term set when used herein may include one or more items. Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Additionally, some of the described method embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently.
An embodiment may develop a model of cable measurements, each cable measurement including multiple data points, values or components. The model may include groups, each group having a centroid and corresponding to a status, such as normal, high alarm, low alarm, warning, etc. For a newly received cable measurement including one or more components, that measurement may be compared to the groups to determine a deviation or likely probability that the new measurement conforms to a group: the group having or associated with the highest probability of including the measurement may be chosen as the status and if needed an alert with the status may be presented to a user. The alert may include the component of the measurement that is most likely to result in the status. That component may be the component with the smallest distance (e.g. Euclidian) from the chosen status or alert group.
Reference is made to
Operating system 115 may be or may include any code segment (e.g., one similar to executable code 125) designed and/or configured to perform tasks involving coordination, scheduling, arbitration, controlling or otherwise managing operation of computing device 100, for example, scheduling execution of software programs or enabling software programs or other modules or units to communicate.
Memory 120 may be or may include, for example, a Random Access Memory (RAM), a read only memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, a volatile memory, a non-volatile memory, a cache memory, a buffer, a short term memory unit, a long term memory unit, or other suitable memory or storage units. Memory 120 may be or may include a plurality of, possibly different memory units. Memory 120 may be a computer or processor non-transitory readable medium, or a computer non-transitory storage medium, e.g., a RAM.
Executable code 125 may be any executable code, e.g., an application, a program, a process, task or script. Executable code 125 may be executed by controller 105 possibly under control of operating system 115. For example, executable code 125 may configure controller 105 to calculate and display cable or connection measurement or anomaly data and perform other methods as described herein. A system according to some embodiments of the invention may include executable code 125 that may be loaded into memory 120 or another non-transitory storage medium and cause controller 105, when executing code 125, to carry out methods described herein.
Storage system 130 may be or may include, for example, a hard disk drive, a CD-Recordable (CD-R) drive, a Blu-ray disk (BD), a universal serial bus (USB) device or other suitable removable and/or fixed storage unit. Data such as user data, survey response data, and survey invitations, may be stored in storage system 130 and may be loaded from storage system 130 into memory 120 where it may be processed by controller 105. For example, memory 120 may be a non-volatile memory having the storage capacity of storage system 130. Accordingly, although shown as a separate component, storage system 130 may be embedded or included in memory 120.
Input devices 135 may be or may include a mouse, a keyboard, a microphone, a touch screen or pad or any suitable input device. Any suitable number of input devices may be operatively connected to computing device 100 as shown by block 135. Output devices 140 may include one or more displays or monitors, speakers and/or any other suitable output devices. Any suitable number of output devices may be operatively connected to computing device 100 as shown by block 140. Any applicable input/output (I/O) devices may be connected to computing device 100 as shown by blocks 135 and 140. For example, a wired or wireless network interface card (NIC), a printer, a universal serial bus (USB) device or external hard drive may be included in input devices 135 and/or output devices 140.
In some embodiments, device 100 may include or may be, for example, a personal computer, a desktop computer, a laptop computer, a workstation, a server computer, a network device, or any other suitable computing device. A system as described herein may include one or more devices such as computing device 100
Reference is now made to
Embodiments of the present invention may include collecting or receiving cable measurement data (e.g. receiving, by the processor 105, as in
Cable or connection properties or measurements may be analyzed and input to a model to determine the severity of degradation of a cable. Modeling, as known in the art, may include the mathematical representation of a process, concept, or operation of a system, often built in a computer program. For example, a model may categorize, group, or cluster cable measurement data according to the severity of degradation of a cable. Categorizing cable measurement data may include collecting or receiving data from a degraded or knowingly faulty cable for the purpose of modeling a certain behavior. For example, a degraded power or data cable may have behavior which exhibits low power output due to the resistance of the cable increasing with age. Behaviors may be modeled by thresholds, a mathematical value by which a certain behavior may be categorized (e.g. grouped). A group may contain multiple types of measurements (e.g. multi-dimension) with a distribution about a centroid defined by a threshold value. As an example, for a particular type of cable, the cable measurement data may first be collected for a new cable of that type under normal conditions (e.g. no extrinsic forces, no degradation, a “new” or “ideal” cable) and modeled for its behavior by a defining threshold. In order to model a different threshold behavior, a similar cable (e.g. same type of cable but degraded or faulty) may be used to collect cable measurement data for another threshold. For example, assume that cable measurement data is obtained to categorize a new cable for “normal” behavior and the cable is provided a steady standardized power input of 0.9 mW at one end of the cable, measuring the cable output power at the other end of the cable may exhibit values in the threshold of 0.8-1 mW. To categorize a degraded cable, the same process may be repeated for the same standardized power input and may exhibit values in the threshold of 0.5-0.7 mW. Each type or class of cable may have data collected from it and the data may be respectively modeled for the cable's behavior, each model grouped and classified by a threshold given the known severity of degradation and respective cable measurement data. To provide consistent and accurate data, some embodiments may provide the same input (e.g. same input voltage) and a standard length of cable (e.g. length of new cable=length of faulty cable) to collect cable measurement data such that the collected cable measurement data is standardized.
Cable measurement data may also be generated, to train a model if cable measurement data for a faulty or degraded cable is unavailable. For example, a cable manufacturer may set temperature limits for a cable it manufactures, such that a cable operating at the temperature limit is considered to be at or close to failure. This limit, or values distributed near this limit may be provided to a dataset (e.g. cable measurement data) to train a model for threshold behavior.
In exemplary embodiments, there may be multiple thresholds for each behavior group, each group, besides the normal group, corresponding to a determined level of cable degradation severity. For example, threshold values for a cable temperature measurement may be considered normal (e.g. no degradation) if it falls within the range of 45-55 degrees C. Additionally, tied to the cable degradation severity may be a corresponding alert. For example, multiple thresholds may be related to the severity of the cable degradation may be set to determine whether a warning or an alert is output. As an example, assume a fiber optic cable has cable measurement data which measures the output laser power of the cable given a known input laser power, where the laser output is the method of data is transmission (light as the means of transmission in fiber optics). The cable's output power may be considered normal if the majority of the cable's output power values fluctuates around a threshold of 1.25 mW, given laser input to the cable of 0.9 mW. This value may be considered to be a part of a normal behavior group, as a normal cable exhibiting normal behavior was measured. However, assume for a degraded cable that there may be an unusually high laser light output power given the same laser input, fluctuating around 1.75 mW: this threshold may be considered part of a high behavior group. Conversely, a degraded cable may have unusually low laser output power fluctuating around 0.25 mW: this threshold may be considered part of a low warning/low alarm behavior group. The unusually high or low cable output values may warrant mere warnings. However, in the situation where a cable is severely degraded, a cable's output power may be extremely high, fluctuating around 2.5 mW, this threshold may be a part of the high warning/high alarm alert behavior group, or extremely low, fluctuating around 0.1 mW, part of the low warning/low alarm alert behavior group. At these levels of severe degradation, it may be necessary to provide not only a warning, but an alert. Cable measurement data may therefore be collected for multiple types of data measurements (e.g. temperature, power, etc.) and grouped or clustered according to the severity of degradation (normal, low warning, low alarm, high warning, high alarm, etc.). The severity of degradation or the known thresholds for certain behaviors may be the basis for grouping or clustering cable measurement data.
A Gaussian (e.g. normal) distribution may be used to model cable threshold and normal behavior, according to one embodiment. Returning to the example of the cable above, cable measurement data and it's threshold behavior may follow a Gaussian distribution for each type of cable measurement data (e.g. temperature, power, etc.). For example, in the above example of the fiber optic cable, the laser output power may fluctuate (e.g. vary) around a certain central value (e.g. the expected value or “centroid”) for a behavior group. Therefore, it may be useful to model each type of cable measurement data with a Gaussian distribution. This may be defined as a random variable X for each degradation severity group (e.g. behavior group). For the sake of simplicity, an example is shown in Table 1 for cable measurement data of ‘laser power output’ for 5 similar cables of the same type with different levels of degradation severity.
Each level of degradation may warrant a respective notification as shown in Table 1. For the Gaussian random variable X, the mean
Therefore, each random variable X for each level of severity may be defined by a Gaussian distribution for a measurement type. As known in the art, the probability density function for the random variable X, that is P(X=x) is given by f(x) with mean
Although a Gaussian distribution may be used to model (and may be the model) cable measurement data for various degradation severities, this is assuming that only one type of cable measurement data is measured (e.g. only measure laser power output). Expanding the dimension to multiple types of cable measurement data poses a problem. This is due to the dependency of cable measurement data upon each other (e.g. correlation or covariance). For example, higher cable voltages inherently produce more heat (e.g. higher temperature), therefore, there may be a correlation between the two cable measurement data such that modeling each cable measurement data individually would not be adequate. For example, assume for an arbitrary example, that for a cable two different types of cable measurement data are collected, voltage and temperature, the probability for seeing a high voltage is 20% and the probability for seeing a low temperature is 20%. This does not show any form of correlation between the cable measurement data. When combined, it should be evident that the probability of such an event should be extremely low (e.g. an event of high voltage and low temperature), much below 20% (e.g. 4%), indicative of an anomaly.
In order to model multiple types of cable measurements together, a multivariate Gaussian distribution may be used. A cable measurement may be a vector including one or more data points, components or values, e.g. an ordered set, where each data point or value is of a specific value such as temperature, power, etc. A set of cable measurement data may include multiple such vectors, each corresponding to a measurement period or point in time. A multivariate Gaussian analysis may vectorize multiple types of cable measurement data, e.g. represented by a matrix where each column is a type of measurement and each row is a sample of multiple measurement. A multivariate Gaussian model may be defined by a random variable X with ap×1 mean vector
Where for p types of cable measurement data, the probability of a vector x (e.g. probability that the set of cable measurement data takes on the values of input row vector x) is given by the above Formula 2. The mean vector
Given this, the probability for any set of cable measurement data may be mapped. For example, for an N=1,000 sample of p=5 types of cable measurement data (e.g. each row vector having length 5, 1,000×5 matrix), the probability density function may be mapped for each sample (e.g. row vector) of the 1,000 samples to a probability using Formula 2. For example, a new data entry may be retrieved in row vector form (e.g. each column a separate type of measurement) and a probability calculated for each behavior group using Formula 2 above.
To complete the model, cable measurement data may be obtained from similar cables of the same type with different levels of degradation, each level of degradation representing a behavior group and modeled by a separate multivariate Gaussian distribution (e.g. each with a mean vector and a covariance matrix). For each behavior group, for each cable measurement type, an expected value may be calculated each expected value combined into a mean vector
To further compact this model, in some embodiments, a multivariate Gaussian distribution may model each group, cluster, or level of degradation as one distribution. As such, with multiple independent multivariate Gaussian distributions, the distributions may be combined into a single distribution for efficient analysis. The multiple behavior groups each modeled by a multivariate Gaussian distributions may be combined into what is known in the art as a Gaussian mixture model (GMM) where the multiple multivariate Gaussian distributions are “averaged”. In other words, “averaged” refers to the total probability not exceeding 100%, therefore the mathematical integral of the GMM model should equal 1. Methods of grouping cable data other than GMM may be used, for example clustering algorithms may be used.
Reference is now made to
In some embodiments, the normal behavior group may be used to calculate a Euclidean distance between the new data entry and the centroid of the normal behavior group. The calculation of the Euclidean distance may indicate which measurement type (e.g. voltage, temperature, laser power, etc.) most significantly affected a cable's behavior from normal. For example, assume a cable measures two cable measurements, voltage and power. Further assume that the retrieved cable data for this cable is distributed around a centroid located at and with the values of: voltage=1V and power=1 mW. A new data entry may be retrieved and may have values such as: voltage=2V and power=3 mW. To calculate the Euclidean distance for each measurement type, the absolute value of the difference between the new data entry and the normal centroid may be calculated, resulting in a Euclidean distance of 1V for voltage (2V−1V) and 2 mW for power (3 mW−1 mW). The cable measurement type which resulted in the largest ratio of change may be indicated as the cable measurement which most affected cable behavior from normal. The largest ratio of change may be, for example, the most influential, “influencer” cable measurement. For example, in the above example there is a 100% ratio of change (2-1/1) for voltage and a 200% ratio of change (3-1/1) for power. Therefore, power was more impactful in this new data entry when compared to voltage. This may provide insight to an analysis, as an increase in power with a disproportionate increase in voltage as exampled may indicate that the conductivity of a cable is increasing (e.g. higher power should be balanced by the voltage squared P=V2/R, if all else equal).
Reference is now made to
At the completion of the data gathering process, at operation 602, a multivariate Gaussian distribution may be used to model each behavior group of cable measurement data for multiple types of cable measurement data. For example, give types of cable measurements may be collected repeatedly for the same type of cable for varying levels of degradation. The set of cable measurement data may be represented for example by a matrix, with each row vector defining each entry of cable measurement data. The multivariate Gaussian model allows for the determination of a mean vector and the covariance matrix, with the mean vector defining the centroid of each respective behavior group and covariance defining the correlation between the groups.
At operation 604, the multivariate Gaussian models may be averaged and combined into one Gaussian mixture model. The calculated centroids determined in operation 602 may therefore be mapped on the Gaussian mixture model.
At operation 606, a new cable measurement data entry may be received. For example, a new data entry may be a row vector specifying the exact values of 5 different types of cable measurement data specified (see
In order to apply the Gaussian mixture model created at operation 604, a probability of anomaly, or the alert, behavior or classification given by a certain group, may be calculated at operation 608. In operation 608, a process may select one of the behavior groups based on the deviation of the new data entry from each of the behavior groups. For example, the probability of anomaly that the new data entry is in each of the low alarm behavior group, low behavior group, normal group, high behavior group, or high alarm behavior group may be respectively calculated. To select the group, the value of the multivariate Gaussian components of the Gaussian mixture model (e.g. related to each centroid) which provides the highest probability e.g. according to Formula 2, hence the smallest deviation for the new data entry, e.g. the group centroid of which the new data entry is “closest” to, may be selected as the behavior group.
In operation 610, the group that was selected in operation 608 may be analyzed against the individual components or values of the new data entry to find the individual component or value that results in the alarm. For example, if the high alarm group is selected, each of the components of the new data entry may have its distance (e.g. Euclidian distance, or another distance measure) compared against that matching (e.g. measuring the same unit) component of the chosen group's centroid, and the new data entry having the furthest distance from the centroid may be selected. Thus if the new data entry results in the “power” component being further from the centroid of the high alarm group than any other component (e.g. temperature, etc.), the alarm may be “high alarm, power”.
At operation 612, an alert is given or displayed based on the selected group. For example, if the new data entry is closest to the centroid of the low behavior group (e.g. highest probability that the new data entry falls within this low group), a low warning may be given.
While certain categories of alarms, e.g. normal, low alarm, etc., are discussed herein, other or different categories or groups may be used. Alerts may be displayed, for example on a computing device, such as a user terminal 210 alongside vital information related to the cable measurement data. Alerts may be used to notify a user real time or displayed to software program based on received cable measurement data. Terminals 210 may be units separate from nodes 204.
Other or different operations may be used.
Reference is now made to
An embodiment may create and aggregate past cable measurement data to recently retrieved cable measurement data to calculate a linear regression line for a model. For example, cable measurement data for cable voltage may be periodically retrieved from a sensor 208. To determine a linear regression, a least squares technique may be implemented on each data point to calculate a line of best fit to determine a slope m. As known in the art, the regression line, also known as “line of best fit” may be modeled by the function y(x) with input x given by example Formula 3 below:
y(x)=m*x+b Formula 3
The letter b in Formula 3 denotes a constant where the line of best fit intersects the y-axis, e.g. when x=0. Of importance is the slope of the line denoted by m which may be monitored over time for a rate of change of slope. The slope of the line as known in the art is the change in the value of y divided by the change in the value of x, this value is a constant value for a linear regression.
To apply the linear regression model, upon each new data entry, the distance of the new data entry from the line of best fit may be determined. When analyzing the new data entry, the further the distance from the line of best fit, the more this value may indicate a trend of anomaly. In one embodiment, to determine the distance, the time of the new data entry may be used to calculate the value of the function compared to the value of the determined line of best fit at the same time. The calculated value from the line of best fit may be subtracted from the new data entry value for the difference. A positive value indicates an upward trend whereas a negative value indicates a downward trend, an equivalent value indicates no trend. As an example, assume for a future time t=1, the value of cable measurement data for voltage is 5V. Additionally, assume a linear regression was modeled for previous voltage cable measurement data by a least squares method and a line of best fit was determined, given by the equation y=3x+4. According to this simple model, the model projects that the value calculates to 7 (e.g. 3*1+4) if it follows the linear regression of past voltage data. However, the new data entry indicates 5V, showing a much lower value than expected. Subtracting the values accordingly results in a distance of −2V (e.g. 5-7), indicating an immediate negative trend with a magnitude of 2V.
In some embodiments, past cable measurement data may be modeled by a Gaussian distribution. For the value of each new data entry, the value may be compared to the modeled Gaussian distribution of past cable measurement data. A threshold may be implemented where any new data entry beyond a standard value requires an alert requires an alert. As an example, assume that cable voltage was measured and a Gaussian distribution modeled with a sample mean of 5 mV and a standard deviation (σ) of 2 mV was calculated. If, for example, a threshold for alert was set at 3 standard deviations (3σ). Any new data entry value beyond 11 mV (5+(2*3)) or below −1 mV (5-(2*3)) would be candidates for alert. This threshold may be set by the user.
Additionally, aggregating the new data entry to the set of modeled cable measurement data may change the slope of the original linear regression line. The rate of this change of slope may be determined. In order to determine a rate of degradation, some embodiments may collect new data entries during a set period of time and may examine the rate of the change of slope from period to period. For example, new data may be collected on a weekly basis and a new linear regression model calculated for the slope m. The change of the slope from week to week, also known in the art as the acceleration or second derivative m′ may be calculated. The higher the value of the second derivative m′ over time, the faster the rate of degradation. The general trend of anomaly may be indicated by the sign of m′, a positive m′ indicates an upward trend whereas a negative m′ indicates a downward trend. As the distance may provide the magnitude of change of the trend of anomaly, the rate of change of slope provides the rate or speed at which this change may be occurring.
Descriptions of embodiments of the invention in the present application are provided by way of example and are not intended to limit the scope of the invention. The described embodiments comprise different features, not all of which are required in all embodiments. Embodiments comprising different combinations of features noted in the described embodiments, will occur to a person having ordinary skill in the art. Some elements described with respect to one embodiment may be combined with features or elements described with respect to other embodiments. The scope of the invention is limited only by the claims.
While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.
