UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering On-line Alert Systems for Production Plants A Conflict Based Approach

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Alert Systems Based on sensor readings, raises a flag in case of an abnormal situation Researchers construct a system using Bayesian Networks to detect abnormal situations and generate alerts

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Typical Production Plant S1S1 S2S2 SnSn C1C1 C2C2 CnCn

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Common Issues Engineers’ knowledge of plant is not sufficient for providing a causal structure Process is too complex to specify the possible faults and how to detect them based on sensor readings Difficult to determine the delay from event to effect Faults are so rare that statistics cannot be used to learn neither the structure nor the parameters of a model of the faults Difference between a true value and its sensor reading; true values should appear as hidden variables

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Causal Approach Collect as much causal structure as possible and combine with a data driven learning method Limitations –Learning algorithms cannot cope with domains with a massive set of hidden variables –It is not obvious how such a model could be used for classifying abnormal behavior

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Proposed Method Learn a Bayesian Network representing normal operation only Does not require information about possible faults or modeling of abnormal behavior Faults are detected by measuring conflict between model and sensor readings

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Proposed Method Consists of two steps: –Learning a model of the sensors for normal operation –Using the learned model to monitor the system, initiate alerts and perform on-line diagnostics

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Learning a Model Can be done in many different ways In this paper the researchers analyzed the database of sensor readings during normal operation where the variables are the sensors and hidden variables are the components of the system

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Initiation of Alerts Sensor readings are received in a constant flow Readings are chopped up into time steps of 1 second Therefore every second we have evidence for every variable in the model

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Conflict Measure of Evidence Let evidence be e = {e 1,…,e n } and the conflict measure of the evidence is:

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Detecting Conflict In general during normal operating we expect: conf(e) ≤ 0 An indication of an abnormal situation is detected when: conf(e) > 0

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Problem with Conflict Measure A negative conflict value does not necessarily imply that we have a normal situation If sensors are strongly correlated during normal operation, the conflict level will be very negative A few conflicting sensors therefore will not cause the entire conflict to be positive To detect watch for jumps in the conflict level

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Tracing Source of Alert Greedy Conflict Resolution –Recursively remove the sensor reading that reduces the conflict the most –Stop when conflict is below a predefined threshold –Can be performed very fast using: fast retraction, lazy propagation or arithmetic circuits

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Conflict Resolution

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Coal Power Plant Network

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Sample Data set Used sample data from normal operation to learn model for coal mill Two data sets covered actual errors/abnormal situations: –The fall-pipe leading coal into the power plant becomes clogged –A temperature sensor becomes faulty

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Clogged Fall-Pipe Data

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Clogged Fall-Pipe Conflict Resolution Indicated that the sensor measuring the water- percentage in the coal can explain all the conflicts There is no sensor for Fall-Pipe clog detection so this is the closest sensor that could explain the conflict Result was consistent with the analysis of the plant Engineers

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Faulty-sensor Data

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Faulty Sensor Conflict Resolution Indicated that six significant sensors could explain the conflict Engineers indicated that four of the six were actually significant, the other two were anomalies due to the model

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Oil Production Facility Simulated cases for normal system operation Two other data sets covered errors/abnormal situations: –Simulated faults in the pumping system –Simulated faults in the cooling system

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Pump and Cooling Data

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Pump Data

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Cooling Data

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Change Point Detection Note that in the previous plots that the conflict measures where all negative indicating no faults However, remember the problem if the sensors are strongly correlated (slide 15) In order to perform conflict resolution the conflict threshold should be based on values observed during normal operation In order to detect changes in system operation need to track jumps in the conflict measure

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Change Point Detection Assume conflict values for normal operation are independent samples from normal distribution with fixed mean and variance Model the l’th conflict value as random variable with normal distribution f where mean  l and variance  l 2 are estimated using the last m observations:

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Change Point Detection To detect change point calculate the logarithmic loss for the last n observations and raise alert in case the value is above a predefined threshold:

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Change Point Detection This approach is sensitive to fluctuations and results in false positives; also has difficulty detecting drifts in conflict measure To alleviate this compare model with another model f’ where mean and variance estimated by shifting s observations back:

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Change Point Detection Compare models (score difference):

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Change Point Detection For normal operation score should be within the interval [-  :  ]  will determine the ratio of false positives and false negatives n determines the response time of the system m determines the relevant history s the number of observations to shift back

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Clogged Fall-Pipe Data (n=m=5, s=20)

UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Clogged Fall-Pipe Data (n=m=10, s=40)