Pressure control

A simplified schematic drawing of the tank pressure control experimental system is shown in Figure 6-6.

Figure 6-6: Tank Pressure Control System

The aim of the control system is to maintain the air pressure in the air tight water tank. This is achieved by varying the inlet flow rate. The inlet flow rate is proportional to the rotational speed of the motorised pump which is controlled by voltage signal from a computer controller. The three monitored variables are flow rate (Qin), level (L), and pressure (P). in Q X L P            (6.26)

Monitored data from these three variables are collected from the flow sensor, level sensor, and pressure gauge respectively. In this case study, the dry down condition of the sump tank is considered as major fault. This condition causes decrease and fluctuation in flow rate which make it difficult to maintain the pressure in the tank. Figure 6-7 shows the behaviour of each system variable when the fault is introduced.

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Figure 6-7: System Responses of Fault

The dry down condition is introduced at 400 seconds and causes sudden drop in flow rate. After the sudden drop, flow rate gradually increases to a steady state. In the meantime, the level and pressure drop drastically when the fault is introduced. The slop of this drop reduces as the flow rate gradually increases. Finally, both level and pressure reach a steady state.

The SOM is trained with both normal operating data and a random fault data to form two clusters. The normal operating data is generated by operating the tank system in a fault free condition for a period of time. The random fault data is generated by introducing random deviations in the obtained normal operating data. Monitored data from the dry down condition is then mapped on the trained SOM. The BMU for each data sample is computed. A trajectory is formed by connecting all the BMUs as seen in Figure 6-8 (a).

Risk-based Fault Detection using Self-Organizing Map

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Figure 6-8: Tank Pressure Control SOM Trajectories

This trajectory represents BMUs in which all data samples are mapped. Data samples with high similarity are mapped in one BMU. Those BMUs with more data samples mapped represent significant variation of the system. Conversely, those BMUs with less data samples mapped represents less significant variations and are disregarded. In this case, it is considered BMUs with less than 10 data samples mapped are less significant and are filtered out from the SOM. The filtered trajectory is shown in Figure 6-8 (a). As compared to Figure 6-8 (a), Figure 6-8 (b) clearly demonstrates the progression of system state from normal to fault condition. Subsequently, the loading vector corresponding to each remaining BMU is calculated using Equation(6.9). Figure 6-9 shows the variation of loading of each variable.

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Figure 6-9: Dynamic Loading of Each Variable

At 400 seconds, the loading of flow rate increases drastically indicating increase in contribution to the fault condition. After 1400 seconds, the system reaches a steady state. The loading of each variable restores to the original condition as before 400 seconds. The dynamic behaviours of the loading not only give an early indication of fault but also facilitate the identification of the root cause. In this case, the root cause of this fault is identified to be directly related to the flow rate.

Next, the mean and standard deviation of the coordinates within the normal cluster are determined. The probability of the trajectory exceeding the normal cluster and the exceedance are calculated based on the predicted coordinates using Equations (6.13) and(6.14). To determine the intensity of the fault, an intensity factor is assigned to each system variable. In this case, the following intensity factors are assigned to the monitored variables. 2 1 3 in Q L P a a a a      _  _  _    (6.27)

Flow rate is given the highest intensity factor as large fluctuation in flow rate can cause damage to the flow meter and a sharp increase in pressure. Pressure is given the second highest intensity factor; a high pressure could cause damage to the pressure gauge, however, in this case, it is regulated by a pressure relief valve. Level is given the lowest intensity factor as it possesses minimum hazard potential to the system. The intensity of the fault is calculated using Equation(6.16). The severity of the fault is calculated using Equation(6.17). Finally, the risk of the system is determined by Equation (6.18) and is plotted in Figure 6-10.

Risk-based Fault Detection using Self-Organizing Map

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Figure 6-10: System Risk Based on Predicted Coordinates

The risk of fault gives a measure of the potential impact of fault on the system. As shown in Figure 6-10, the risk of fault spikes at 400 seconds which is the exact moment of the fault occurring. The risk increases as the fault progresses indicating an increasing potential impact. This new approach shows a very high sensitivity of change of system state. It is able to detect and assess the potential impact of fault at its early stage.

In the next stage, the risk of fault is broken down into different levels to enable a refined monitoring of the system and an efficient determination of remedial actions and safety measures. The risk-based monitoring of the tank pressure control system is shown in Figure 6-11.

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Figure 6-11: Risk-based Monitoring of the Pressure Control System

The system is operating within the range of normal state until approximately 630 seconds. After 630 seconds, the system deviates out of the normal state and enters the control state. The risk of fault increases up to 9 and stays steady until 820 seconds. By taking proper control action, risk of the fault could be brought back to normal. In this case, no control actions are taken, the risk of fault continues to increase, eventually leading to the system operating in the warning state. After 1400 seconds, the operation of the system becomes stabilized. The risk of fault becomes steady and stops increasing further into the shutdown state.

The proposed approach has demonstrated high sensitivity of change of system’s state in this case study. The fault is detected at the moment of occurring. In the meantime, the root cause of the fault is identified to be directly related to the flow rate at the moment of fault occurring. The breakdown of risk into different levels allows refined monitoring of system operating states. This provides the advantage in early deployment of remedial actions or safety measures to minimize the risk of an accident.