Modeling of Vibration Monitoring of Steam Turbine in Nuclear Power Plant using Modular Artificial Neural Network

Transcription

1 Modeling of Vibration Monitoring of Steam Turbine in Nuclear Power Plant using Modular Artificial Neural Network Mohammed M. Zahra *, Lamiaa K. Abd Elaziz ** and Hassan M. Fahmi** *Electrical Communications Engineering Department, Faculty of Engineering, Al Azhar University, Cairo, Egypt **Operational Safety and Human Factors Department, National Center for Nuclear Safety and Radiation Control, Atomic Energy Authority, Cairo, Egypt. Received: 20/4/2011 Accepted: 19/5/2011 ABSTRACT This paper states a methodology for using a Modular Artificial Neural Network (ANN) in modeling the vibration monitoring of the Steam Turbine (ST) in Nuclear Power Plant (NPP). The input and the output signals of the vibration transducer are used as a source of the training data for the neural network model. The type of the network used in this methodology is the supervised Multilayer Feed- Forward Neural Networks with the Back-Propagation (BP) algorithm. The module architecture is according to the Human Factors (HF) Considerations in designing the Human-System Interface (HSI). The Vibration Severity limits are determined by the International Organization for Standardization (ISO) The model also contained 2out of 3 voting and dynamic trip limit value ANNs. The results show that the proposed Modular ANN has good generalization capability to monitor and protect the machine from the Vibration Severity, increasing the reliability of (ST), and good HSI. This modeling methodology can be used for the other non-redundant components in NPP such as Reactor Coolant Pump (RCP). Key Words: Vibration Severity / Steam Turbine / ISO / Human Factors /Modular ANN. INTRODUCTION Vibration in ST can be caused by a wide variety of problems related to rotating equipments. Such as; Unbalance, Misalignment and Rolling element bearing problems. Vibrations are vital indicators of machinery health. The 24.6% of the problems in RCP were directly caused by vibration and another 5.2% were indirectly caused by vibration (1). The machinery vibration monitoring are effective in reducing overall operating costs of industrial plants by monitoring vibration levels over time that allows the plant engineer to predict problems before serious damage occurs. According to machinery monitoring there is a philosophy of Maintenance approaches known as Predictive Maintenance (Condition-Based Maintenance). Studies shows that the costs of Predictive Maintenance is about $9/hp/yr (horse power per year) where it is $13/hp/yr for Preventive Maintenance(Time-Based Maintenance) and $18/hp/yr for Reactive Maintenance (Breakdown or Run-to-Failure Maintenance) (2). Steam Turbine is playing an important role in power generation in NPP. The steam is routed to the ST where it is partly expanded in a high pressure (HP) stage; the energy is extracted from the steam as it passes through ST blades. The steam is returned to the reheating to improve efficiency, after which it is returned to the ST to continue expanding and extracting energy in Low pressure (LP) stages. Large vibrations in ST will initiate a turbine trip signal. That signal is delayed to allow time for intended systems to operate to overcome this problem (3). If these systems are failed the RPS (Reactor Protection System) starts to shutdown the reactor.

2 ISO (4) is a Broad band vibration measurement used for the diagnosis of general machine condition. ISO Standards provide guidance for evaluating vibration severity in machines operating in the 10 to 200 Hz frequency range, it separates the working Vibration into four zones; Green, Yellow, Orange and Red. It also defines four classes of machines, according to their size and mounting conditions, fig.(1). Fig. (1): ISO10816 standard Despite the incorporation of advanced technology into NPP alarm systems, the HFs problem remains. The operators obtain information and take actions through the HSI to perform their tasks. Alarm reduction processing methods (e.g. Alarm Prioritization, Alarm Filtering and Alarm Levels Increasing) (5),Two Alarm Cause Tracking Method and Level Precursor Processing method (6) are Some trends used in HSI evolution and designing. Realizing the importance of the continuous availability of the non-redundant components in NPP.An online vibration monitoring system for those components has been installed, for example: On-line Vibration Monitoring System for the Main Coolant Pump NO-3 of Dhruva Reactor (7). On-line Monitoring of Turbo-Generator Vibration KAKRAP-1 (8). The North Anna Power Station has installed a monitoring system for most machines in the station, the goal is to reduce the forced outage and maximize the utilization and availability of the plant (9). The applications of neural networks in nuclear engineering can be seen in many works such as: modeling and diagnostics. ANNs provides the capability to do Black Box Modeling with little or no prior knowledge of the system function but it needs input/output map only. The applications areas of ANNs in enhancing the safety and efficiency of NPP are: diagnosis of abnormal conditions, detection of the change of mode of operation, alarm Processing, monitoring of check valves, monitoring of plant parameters and analysis of plant vibrations (10). A three-layer ANN is proposed for the prediction of the effect of combined faults of unbalance and shaft bow on the frequency components of vibration signature of the rotating machinery. It is found that marquardt algorithm is much more efficient than

3 the other techniques in training that ANN (11).The wavelet neural network (WNN) is used to predict vibration trend in Wind Turbine. The simulation data test result shows that WNN has great advantages than BP network in predicting the characteristic vibration parameters of the deterministic trend (12). In this paper the vibration monitoring and protection system of NPP ST is modeled using Modular ANN, i.e. an ANN composed by several other ANNs (13). The type of neural network structure used here is the feed-forward multilayer network with the back-propagation learning algorithm. The input data of the ANNs is the measured current value of the vibration sensor; the ANNs processes that data and gives the outputs of the module which are the vibration value monitoring, alarm and trip signals. The alarm and trip signals limits are based on ISO standards for vibration severity. The output topology is based on HF considerations for evolution HSI in NPP. This paper is organized as follows: Section 2 presents the modular ANN modeling methodology. The test and results of the network is presented in Section 3. Finally, Section 4 presents the conclusions. 1. Network Architecture: ANN MODELING METHODOLOGY The functions of the ANN are monitoring the vibration value using three transducers, alarm signal to inform the operator to take any corrective action in case that the value of the vibration increased than the limited value and trip the machine if the vibration is still increased and the operator did not shutdown the machine manually. The outputs of the module are placed in three tracks according to the HSI considerations which are monitoring, transducers degradation and component degradation. The vibration modular ANN block diagram is shown in fig.(2).it consists of six ANNs whose functions are: One for the Direct Monitoring of the vibration value of the transducers (Vt1, Vt2, Vt3) which facilities the operator to observe the value vibration for each transducer. Three for the Alarm/Trip estimation, for each transducer the alarm/trip ANN compares the transducer value with the setting points. If the value is greater than alarm setting point then set Alr=1, and if the value is greater than trip setting point then set T=1. One for the Alarm voting and Transducers Degradation, which has three inputs (Alr1, Alr2 and Alr3) and four outputs. Three outputs (Vt1d, Vt2d and Vt3d) for Transducer Degradation track which identify the degraded Transducer. The fourth output is the Alarm which indicates component degradation. The ANN function is to decide that the alarm signal is due to transducer degradation or due to ST degradation (14). The ANN uses the 2out of 3 voting strategy to increase the reliability of the module. One for the Trip voting, which has three inputs (T1, T2, T3) and one output; the trip signal. Under the criteria 2 / 3 the trip signal will shutdown the ST to protect it from damage. 2. Training Data and Preparation: The module uses the 4-20 ma Accelerometers vibration transducers as a source of the training data. The vibration transducers are placed at strategic points in Turbine/Generator (T/G) shaft. Each transducer converts the vibration velocity in form of Fatigue (which is a Repeated cycles of stress on a component) into electrical signal by sensing element e.g. Piezoelectric. This type measures the overall amplitude of vibration (root mean squared inch per sec (IPS)) within a specified frequency range. The Overall Vibration measures a normal and low frequency machine vibration which detects rotational and structural problems like imbalance, misalignment, and mechanical looseness. The data will be 466

4 normalized because the ANN works better when input and output lie between 0-1. The module uses the min-max normalization which performs a linear transformation on the original data values (15). 3. Alarm and Trip Level Calculation: Alarm Limit Level: The alarm limit value will be minimized as possible to early notify the operator to take the corrective action and in contrast trip limit value will be maximized as possible to verify the corrective action, minimize the false alarms which disturb the operator and to avoid the wrong shutdown action. Fig.(1) shows that the ST belongs to class Ιv because it is a large soft foundation. There have been attempts to establish limits of diagnostic parameters for the Maria Research Reactor and concluded that the setting of the trip and alarm limits values should be done rather on practical than theoretical basis (16).There are more than one method for that calculation one of them are used in (17). The limit value is calculated by the following equation: Limit Value = [Mean + (4 x Standard Deviation)] Where the mean and standard deviation of the given data set e.g. alarm limit values. The study in (18) concluded that the alarm limits for RCP should be R=2 where R =Vd/Vb, Vd is the measured value of a vibration parameter in a defect Condition and Vb is the baseline vibration reference value. Based on the ISO10816 standard R should be between 2.5 and 3.So the module used R=2.5 for alarm limit and R=3 for trip limit. Fig. (2): Vibration modular ANN block diagram Trip Limit Level: To avoid the false reactor Trip action like that occurred in Unit 1 of Oconee Nuclear Station due to incorrect indications of increasing reactor coolant pump vibration (19) the trip limit value should be too reliable than the alarm signal. This will avoid shutdown the machine which interrupts the reactor. So, the module applies the following rules on the trip action signal: 1- The 1 st trip signal is activated directly when the vibration trip limit value is reached. 2- The 2 nd and 3 rd trip signals are delayed by activated when the vibration trip limit value is still and increased to 10 percent of the vibration trip limit value. 3- Finally the trip action decision is assured by using the 2out of 3 voting criteria to increase the reliability of the module.

5 The module applies on a 970 Mega watts ST that rotates at 1800 revolutions per minute (9). Suppose that the baseline vibration value is 0.09 IPS, the ST trip limits are calculated as follows: 4. Train the network: Alarm limit = baseline values x 2.5 = IPS Alarm limit (for Trip calculation) = baseline values x 3 = 0.27 IPS 1st Trip limit = Alarm limit x 3 = 0.81 IPS 2nd Trip limit = 10% x (1st Trip limit) + 1st Trip limit = IPS 3rd Trip limit = 2nd Trip limit = IPS Once the network is constructed it becomes ready to be trained. Each set of input signals is sent through the network and the output signals obtained are compared with the known output signals (supervised mode); based on the comparison the total error is calculated. The connection weights are adjusted through the back-propagation algorithm, to minimize the error. This process is repeated until the total error is less than the minimum allowed error. The ANN module contains 29 sigmoid activation function neurons as processing neurons excluding the 12 linear function which are in input layer. The numbers of training patterns are 19. The network is trained with epoch 5000, and 15000, the root mean squared error are , and respectively. The large number of epochs is due to the vibration values are very small and the ANN network should be very sensitive to changes in them. TEST AND RESULTS The network is tested in two cases, in the 1 st case it is supposed that the three transducers read the same value while in the 2 nd case the three transducers read different values. Case 1: Table (1) shows the readings of the three sensors. Fig. (3) shows the output of the modular ANN. the 1 st trip is activated but the Trip Action doesn't activate instantaneously. Due to the delayed time which prevents the false Trip Action due to transient vibration value. Table (1): Modular ANN input case 1 Transducers Reading values Vt1,Vt2,Vt Vt1,Vt2,Vt

6 Fig. (3): Modular ANN outputs for case 1 Case 2: For input test patterns where the transducers output differs, the outputs of the ANN are illustrated in fig. (4).The results of these input data are: 1. The three transducers degradation are illustrated in table (2). the ANN degradation network activated when one of the three sensor differs from the other sensors, Also the source degradation is based on Alarm action not in monitoring value, this should minimize the activation numbers of sources degradation alerts which may annoying the operator. 2. Finally, the action should be taken by the operator (based on Alarm signal) or without him but by the system itself (based on Trip Action) is shown in fig. (4). Referring to the figure, it seams that the Alarm and the Trip Action is assured by 2 out of 3 ANN, this explain why the machined didn't tripped when trip1 (1 st trip) is activated. Table (2): Transducers Reading and Modular ANN outputs Vt1 Vt2 Vt3 Vt3d Vt1d Vt2d Alarm 1 Mon Mon Mon st trip Trip action E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E Vt1 Mon= sensor no.1 monitoring value and Vt1d= sensor no.1 degradation

7 Fig. (4): Modular ANN outputs for case 2 CONCLUSION This paper introduces a model for vibration monitoring of ST in NPP using BP ANN algorithm. The paper states how the HF consideration is applied to HIS designing.the model has been tested for two cases; one when the transducers values are the same and the other when they differ. The results show that, the linear activation for input layer (data ranged from 0 to1) in ANN is better than sigmoid activation due to it's large range. The best selections of the normalization method will minimize the number of neurons in the ANN and it is better for monitoring the vibration value. The proposed strategy for the trip signal gives good results than the passive delay time strategy because the proposed one depends on the increase rate of vibration. REFERENCES (1)Young-Chae Bae, Hee-Soo Kim, Jae-Hong Jeong, Hyun Lee and Tae-Ryong Kim, Diagnosis of Abnormal Vibrations for Reactor Coolant Pump,SMiRT-15,1999. (2)G. P. Sullivan,R. Pugh,A. P. Melendez,W. D. Hunt, Operations & Maintenance Best Practices: A Guide to Achieving Operational Efficiency, U.S. Department of Energy, (3) Preliminary Safety Analysis Report for Lungmen Nuclear Power Plant units 1#2, (4)"Vibrations Monitoring Recommendations Overview", Innovatorium Technologies Corporation, (5) John M. O hara, James C. Higgins and William S. Brown, "Identification and Evaluation of Human Factors Issues Associated with Emerging Nuclear Plant Technology", Nuclear Engineering and Technology, Vol.41 Nol.3 April (6)Jung-Woon Lee, Jung-Taek Kim, Jae-Chang Park, In-Koo Hwang, and Sung-Pil Lyu, Computerbased Alarm Processing and Presentation Methods in Nuclear Power Plants"World Academy of Science, Engineering and Technology 65, (7)A. Ramarao, "Online Condition Monitoring System for the Main Coolant Pump No-3 of Dhruva Reactor", Reactor Technology & Engineering, (8)C. K. Pithawa and A. Rama Rao, "On-Line Monitoring of Turbo Generator Vibration in KAKRAPAR-1", Koria,2008. (9)D.N. Brown, "Machine-Condition Monitoring using Vibration Analysis, A Case Study from a Nuclear Power-Plant",

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