For a plurality of sensors, a particular sensor is indicated as a target sensor and the other sensors as input sensors. A regression model is trained using historical data from the plurality of related sensors. The trained regression model is applied to the target sensor to generate a predicted target sensor value. A difference between an actual target sensor value and the predicted target sensor value is calculated. A probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value is compared against a threshold value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: indicating, for a plurality of related sensors, a particular sensor as a target sensor and the other sensors as input sensors; training a regression model using historical data from the plurality of related sensors; applying the trained regression model to the target sensor to generate a predicted target sensor value; calculating a difference between an actual target sensor value and the predicted target sensor value; and comparing a probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value against a threshold value. 2. The computer-implemented method of claim 1, comprising generating expected sensor values of the target sensor in a normal status by applying the historical data to the regression model. 3. The computer-implemented method of claim 2, comprising generating a probability model based at least on a measured difference between the expected sensor values and actual sensor values. 4. The computer-implemented method of claim 3, comprising applying the probability model to calculate the probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value. 5. The computer-implemented method of claim 3, wherein the probability model is a Gaussian mixture model. 6. The computer-implemented method of claim 1, comprising: initiating an alarm state if the comparison is below the threshold value; and indicating a normal status if the comparison is above the threshold value. 7. The computer-implemented method of claim 1, wherein the threshold value is dynamically determined. 8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: indicating, for a plurality of related sensors, a particular sensor as a target sensor and the other sensors as input sensors; training a regression model using historical data from the plurality of related sensors; applying the trained regression model to the target sensor to generate a predicted target sensor value; calculating a difference between an actual target sensor value and the predicted target sensor value; and comparing a probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value against a threshold value. 9. The non-transitory, computer-readable medium of claim 8, the operations comprising generating expected sensor values of the target sensor in a normal status by applying the historical data to the regression model. 10. The non-transitory, computer-readable medium of claim 9, the operations comprising generating a probability model based at least on a measured difference between the expected sensor values and actual sensor values. 11. The non-transitory, computer-readable medium of claim 10, the operations comprising applying the probability model to calculate the probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value. 12. The non-transitory, computer-readable medium of claim 8, wherein the probability model is a Gaussian mixture model. 13. The non-transitory, computer-readable medium of claim 8, the operations comprising: initiating an alarm state if the comparison is below the threshold value; and indicating a normal status if the comparison is above the threshold value. 14. The non-transitory, computer-readable medium of claim 8, wherein the threshold value is dynamically determined. 15. A computer system, comprising: a computer memory; and a hardware processor interoperably coupled with the computer memory and configured to perform operations comprising: indicating, for a plurality of related sensors, a particular sensor as a target sensor and the other sensors as input sensors; training a regression model using historical data from the plurality of related sensors; applying the trained regression model to the target sensor to generate a predicted target sensor value; calculating a difference between an actual target sensor value and the predicted target sensor value; and comparing a probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value against a threshold value. 16. The computer system of claim 15, the operations comprising generating expected sensor values of the target sensor in a normal status by applying the historical data to the regression model. 17. The computer system of claim 16, the operations comprising generating a probability model based at least on a measured difference between the expected sensor values and actual sensor values. 18. The computer system of claim 17, the operations comprising applying the probability model to calculate the probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value. 19. The computer system of claim 17, wherein the probability model is a Gaussian mixture model. 20. The computer system of claim 15, the operations comprising: initiating an alarm state if the comparison is below the threshold value; and indicating a normal status if the comparison is above the threshold value.