# US 12,181,379 B2 · Anomaly detection and failure prediction for predictive monitoring of industrial equipment and industrial measurement equipment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,181,379 B2 - Document ID: US12181379B2 - Status: Granted - Kind: B2 - Application number: 17/853,491 - Filed: 2022-06-29 - Published: 2022-12-29 - Granted: 2024-12-31 - Earliest claimed: 2021-06-29 - Assignees: Spartan Controls Ltd. - Applicants: Spartan Controls Ltd. - Inventors: Rahul Raveendran; Hailei Jiang - Web page: https://patentgrep.com/patent/US12181379B2 ## CPC - G01M 99/005: {Testing of complete machines, e.g. washing-machines or mobile phones (testing of machine parts G01M13/00; testing of electric apparatus or components G01R31/50)} - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR ## Abstract A system and method for predicting the failure and estimating the health of the industrial equipment or industrial measurement equipment and diagnosing the root cause of the incipient failures is provided. The system and method are applicable to different types of flow meters used in flow measurement applications and other industrial equipment or industrial measurement equipment. A means to detect anomalies in the industrial equipment or industrial measurement equipment diagnostic signals is provided and thereby allowing incipient failure prediction. The system and method provides the means to calculate the current state of health of the industrial equipment or industrial measurement equipment. Leading indicator/failed component in the industrial equipment or industrial measurement equipment can be tracked down in the event of a failure. The system and method provides the means to label the normal and abnormal periods in the historical data based on the available diagnostic alarms and signals. ## Claims ### Claim 1 A method of anomaly detection and failure prediction for predictive monitoring of industrial equipment or industrial measurement equipment of an associated process, the method comprising: receiving a plurality of historical diagnostic measurements of respective measurement signal groups associated with a respective industrial equipment or industrial measurement equipment of the associated process; generating a detection model for each measurement signal group associated with the industrial equipment or industrial measurement equipment, defining statistical thresholds for operation of each measurement signal group derived from the plurality of historical diagnostic measurements; receiving real-time diagnostic measurements from each measurement signal group associated with the respective industrial equipment or industrial measurement equipment; scoring the received real-time diagnostic measurements using the generated detection model to generate performance metric scores for each of a major space projection and a null space projection of the detection model as a percentage of variability from the detection model; and identifying one of the performance metric scores for each measurement signal group to detect anomalies or predict potential failure of the industrial equipment or industrial measurement equipment selected from a maximum score from one of the major space projection or the null space projection. ### Claim 2 (depends on 1) The method of claim 1 further comprising: generating a predicted failure time for the industrial equipment or industrial measurement equipment from the received diagnostic measurements and the generated detection model. ### Claim 3 (depends on 1) The method of claim 1 further comprising: receiving historical process status information; associating the historical process status information with an operating mode of the process; and associating the operating mode of the process in the detection model. ### Claim 4 (depends on 1) The method of claim 1 wherein plurality of historical diagnostic measurements are grouped into signal grouping based on an associated weighting factor, wherein each group is associated with a respective detection model. ### Claim 5 (depends on 4) The method of claim 4 wherein the respective detection model is weighted with an importance metric identifying contribution to identifying performance of the measurement signal group. ### Claim 6 (depends on 5) The method of claim 5 wherein the statistical thresholds are generated from the detection model for each industrial equipment or industrial measurement equipment and associated measurement signal group. ### Claim 7 (depends on 1) The method of claim 1 further comprising generating a health score based upon a combination of performance metric scores of each measurement signal group. ### Claim 8 (depends on 4) The method of claim 4 further comprising generating a root cause analysis tree based on the statistical thresholds on the performance metric score and component level diagnostic signals. ### Claim 9 (depends on 1) The method of claim 1 further comprising generating a time to failure model. ### Claim 10 (depends on 1) The method of claim 1 wherein the plurality of historical diagnostic measurements and real-time or near-real-time diagnostic measurements associated the respective industrial equipment or industrial measurement equipment are remotely located and received through a network. ### Claim 11 (depends on 10) The method of claim 10 wherein the network is a wireless network. ### Claim 12 (depends on 10) The method of claim 10 wherein the network is a wired network. ### Claim 13 (depends on 1) The method of claim 1 further comprising correlating the plurality of historical diagnostic measurements if performed for multiple industrial equipment or industrial measurement equipment. ### Claim 14 (depends on 1) The method of claim 1 further comprising correlating the plurality of historical diagnostic measurements is performed for multiple industrial equipment or industrial measurement equipment associated with a respective model, type or brand identification. ### Claim 15 (depends on 1) The method of claim 1 further comprising labelling normal and abnormal periods in the historical diagnostic measurements based on diagnostic alarms and signals available from the industrial equipment or industrial measurement equipment. ### Claim 16 A method of anomaly detection and failure prediction for predictive monitoring of ultrasonic flow meters of an associated process, the method comprising: retrieving a detection model generated from historical measurements of a measurement signal group associated with the ultrasonic flow meters; receiving real-time diagnostic measurements associated with the ultrasonic flow meters of the associated process; scoring the received real-time diagnostic measurements using the detection model to generate performance metric scores for each of a major space projection and a null space projection of the detection model of the detection model as a percentage of variability from the detection model; identifying one of the performance metric scores for each measurement signal group to detect anomalies or predict potential failure of the ultrasonic flow meters, selected from a maximum score from one of the major space projection or the null space projection; and displaying the identified performance metric score and associated parameter primarily contributing to the performance metric score for a measurement signal group of the ultrasonic flow meter. ### Claim 17 (depends on 16) The method of claim 16 further comprising displaying a root cause analysis for a selected measurement signal group of the ultrasonic flow meter. ### Claim 18 A system comprising: a processor; a network interface coupled to the processor for receiving diagnostic measurements from a plurality of industrial equipment or industrial measurement equipment associated with a process; and a memory containing instruction which when executed by the processor perform: receiving a plurality of historical diagnostic measurements of respective measurement signal groups associated with a respective industrial equipment or industrial measurement equipment of the associated process; generating a detection model for each measurement signal group associated with the industrial equipment or industrial measurement equipment, defining statistical thresholds for operation of each measurement signal group derived from the plurality of historical diagnostic measurements; receiving real-time diagnostic measurements from each measurement signal group associated with the respective industrial equipment or industrial measurement equipment; scoring the received real-time diagnostic measurements using the generated detection model to generate performance metric scores for each of a major space projection and a null space projection of the detection model as a percentage of variability from the detection model; and identifying one of the performance metric scores for each measurement signal group to detect anomalies or predict potential failure of the industrial equipment or industrial measurement equipment selected from a maximum score from one of the major space projection or the null space projection. ### Claim 19 (depends on 1) The method of claim 1 wherein the industrial measurement equipment is an ultrasonic flow meter or the industrial equipment is a rotating industrial equipment. ### Claim 20 (depends on 1) The method of claim 1 wherein the performance metric score is selected from one of the major space projection or the null space projection based on a significance of violation of the respective spaces.