MADE 3.9.1 User Manual > Sensors
Sensor Parameters
| Sensor Parameter | Description | Equations |
|---|---|---|
| Probability of Detection (POD) | True Positive Rate. Proportion of positives that are correctly identified. The probability that a failure mode (high or low flow response) when present, will be correctly identified as such. This is calculated as the True Positives as a proportion of all positive conditions. | [Image: POD.png] |
| Specificity (SPC) | The probability that the condition being sensed is not present, given a negative reading. The probability that a nominal response when present, will be correctly identified as such. This is calculated as the True Negatives as a proportion of all negative conditions. | [Image: SPC.png] |
| False Negative Rate (FNR) | The probability that a failure mode (high or low response) when present , will be incorrectly identified as nominal. The rate of False Negatives as a proportion of all positive responses. | [Image: FNR.png] |
| False Alarm Rate (FAR) | The rate of False Positives as a proportion of all negative responses. The probability that a nominal response when present, will be incorrectly identified as a failure mode (high or low response). | [Image: FAR.png] |
| Positive Predictive Value (PPV) | The precision of the test. Calculated as the True Positives as a proportion of all positive responses. The probability a failure mode will be present when the sensor signals that a failure mode is present. | [Image: PPV.png] |
| Negative Predictive Value (NPV) | The True Negative responses as a proportion of all negative responses. The probability a nominal response will be present when the sensor signals that a nominal response is present. | [Image: NPV.png] |
| Accuracy (ACC) | The number of correct responses as a proportion of the total responses, as a percentage. The probability that the sensor will correctly identify any response (either a failure mode or nominal). | [Image: ACC.png] |
| Balanced Accuracy (BACC) | The accuracy of the test calculated in a way that avoids inflated accuracy values where the dataset is imbalanced (such as when the situation under test has a very low occurrence). Average of proportion of sensor predictions that correctly identify a failure mode and the proportion of sensor predictions that correctly identify a nominal response. | [Image: BACC.png] |
| Likelihood Ratio Positive (LRP) | The LRP is used to determine how useful the sensor is in detecting the presence of the condition. A value higher than 1 indicates that the test is associated with the condition. The higher the value, the more probable it is that the condition is present when the sensor indicates so. | [Image: LRP.png] |
| Likelihood Ratio Negative (LRN) | Likelihood Ratio Positive. The LRP is used to determine how useful the sensor is in detecting the presence of the condition. A value higher than 1 indicates that the test is associated with the condition. The higher the value, the more probable it is that the condition is present when the sensor indicates so. | [Image: LRN.png] |
| Matthews Correlation Coefficient (MCC) | The MCC provides a value which represents the predictive ability of a sensor. This value is between 1 and -1, a value of 1 represents perfect prediction, a value of -1 represents total disagreement. A value of 0 indicates a no better than random prediction. | [Image: MCC.png] |
Source: Local MADE 3.9.1 installation: com.phm.made.help.plugin/documents/help/html/MADeHelp/15-Sensors/15-12-SensorParameters.html · retrieved 2026-07-09