Hybrid model for fault detection and diagnosis in an industrial distillation column

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Julia Picabea ◽  
Mauricio Maestri ◽  
Miryan Cassanello ◽  
Gabriel Horowitz
Keyword(s):  
Fault Detection ◽  
Hybrid Model ◽  
First Principles ◽  
Distillation Column ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Equivalent Model ◽  
Wide Range ◽  
Fault Detection And Identification ◽  
State Library

AbstractThe present work describes a method of automatic fault detection and identification based on a hybrid model (HM): First Principles – Neural Network. The FPM can simulate a wide range of situations while the NN corrects the model output using information from the historical data of the process. Operating conditions corresponding to different types of faults were simulated with the HM and saved with their description in a process state library. To detect a fault, the online measured data was compared with that corresponding to the operation under normal conditions. If a significant deviation was detected, the current state was compared with all the states stored in the process state library and it was identified as the one at the shortest distance. The method was tested with real data from a methanol-water industrial distillation column. During the studied period of operation of the plant, two faults were identified and reported. The proposed method was able to identify such failures more effectively than an equivalent model of first principles. The results obtained show that the proposed method has a great potential to be used in the automatic diagnosis of faults in refining and petrochemical processes.

A Natural Convection Fin with a Solution-Determined Nonmonotonically Varying Heat Transfer Coefficient

1981 ◽  
Vol 103 (2) ◽  
pp. 218-225 ◽  
Author(s):  
E. M. Sparrow ◽  
S. Acharya
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Natural Convection ◽  
Transfer Coefficient ◽  
First Principles ◽  
Numerical Solutions ◽  
Flow Direction ◽  
Operating Conditions ◽  
Fluid Temperature ◽  
Wide Range

A conjugate conduction-convection analysis has been made for a vertical plate fin which exchanges heat with its fluid environment by natural convection. The analysis is based on a first-principles approach whereby the heat conduction equation for the fin is solved simultaneously with the conservation equations for mass, momentum, and energy in the fluid boundary layer adjacent to the fin. The natural convection heat transfer coefficient is not specified in advance but is one of the results of the numerical solutions. For a wide range of operating conditions, the local heat transfer coefficients were found not to decrease monotonically in the flow direction, as is usual. Rather, the coefficient decreased at first, attained a minimum, and then increased with increasing downstream distance. This behavior was attributed to an enhanced buoyancy resulting from an increase in the wall-to-fluid temperature difference along the streamwise direction. To supplement the first-principles analysis, results were also obtained from a simple adaptation of the conventional fin model.

Selection of Mother Wavelet for Effective Wavelet Transform of Bearing Vibration Signals

2014 ◽  
Vol 1039 ◽  
pp. 169-176 ◽  
Author(s):  
H.S. Kumar ◽  
P. Srinivasa Pai ◽  
N.S. Sriram ◽  
G.S. Vijay
Keyword(s):  
Wavelet Transform ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Variable Load ◽  
Mother Wavelet ◽  
Vibration Signals ◽  
Time Frequency ◽  
Deep Groove ◽  
Wide Range ◽  
Bearing Vibration

Condition monitoring (CM) and fault diagnosis of equipments has gained greater attention in recent years, due to the need to reduce the down time and enhance the life/ condition of the equipments. The rolling element bearings (REB) are the most critical components in rotary machines. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance activity. Vibration signal analysis provides wide range of information for analysis. So in this paper, vibration signals for four conditions of a deep groove ball bearing namely Normal (N), bearing with defect on inner race (IR), bearing with defect on ball (B), and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under maximum speed and variable load conditions. Depending on the machinery operating conditions and the extent of bearing defect severity, the measured vibration signals are non-stationary in nature. Non-stationary signals are effectively analyzed by wavelet transform technique, which is a popular and widely used time-frequency technique. The focus of this paper is to select a best possible mother wavelet for applying WT on bearing vibration signals. The two selection criteria includes minimum Shannon entropycriteria(MSEC) and Maximum Energy to Shannon Entropy Ratio criteriaR(s). This helps in effective bearing CM using WT.

A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings

2011 ◽  
Vol 90-93 ◽  
pp. 3061-3067
Author(s):  
Hai Tao Wang ◽  
You Ming Chen ◽  
Cary W.H. Chan ◽  
Jian Ying Qin
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Office Buildings ◽  
Model Parameters ◽  
Time Data ◽  
Model Based ◽  
Air Handling Units ◽  
Detection And Diagnosis

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.

scholarly journals Fuzzy Fault Detection for Permanent Magnet Synchronous Motor

2021 ◽  
Vol 346 ◽  
pp. 03067
Author(s):  
Alexander Romanov
Keyword(s):  
Fault Detection ◽  
Permanent Magnets ◽  
Residual Life ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Detection Scheme ◽  
Synchronous Motors ◽  
Detection And Diagnosis

In the transition to automated and automatic manufacturing an urgent problem is to increase the reliability of mobile robots (MR) and their drives, creation of devices to monitor the technical characteristics of MR, diagnose and predict the remaining resource. Inspite of the high relevance of the diagnosing MR drives problem, there are no generally accepted methodology for diagnosing MR drives, criteria for selecting methods, parameters and volumes of diagnostics at present. An unsolved problem, related to the diagnosis of MR drives and the prediction of their residual life remains, is the development of methods that allow to carry out of automatic complex multiparametric diagnostics and prediction of the residual life using artificial intelligence methods. Effective fault detection and diagnosis can improve the reliability of the MR drive and avoid costly maintenance. In this paper a fault detection scheme for synchronous motors with permanent magnets based on a fuzzy system is proposed. The sequence current components (positive and negative sequence currents) are used as fault indicators and are set as input to the fuzzy fault detector. The expediency of the proposed scheme for determining of various types of faults for a synchronous motor with permanent magnets under various operating conditions is simulated using the SimInTech software.

scholarly journals NARX NETWORK BASED DATA-DRIVEN ALGORITHM FOR DETECTION OF TRAY FAULTS IN NONLINEAR DYNAMIC DISTILLATION COLUMN

2020 ◽  
Vol 82 (5) ◽  
Author(s):  
Syed Ali Ammar Taqvi ◽  
Haslinda Zabiri ◽  
Lemma Dendena Tufa ◽  
Fahim Uddin ◽  
Syeda Anmol Fatima ◽  
...  
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Nonlinear Dynamic ◽  
Distillation Column ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Detection Methods ◽  
Maintenance Cost ◽  
Monitoring And Control ◽  
Process Industries

Efficient monitoring of highly complex process industries is essential for better management, safer operations and high-quality production. Timely detection of various faults helps to improve the performance of the complex industries, prevent various unfavorable consequences and reduce the maintenance cost. Fault Detection and Diagnosis (FDD) for process monitoring and control has been an active field of research for the past two decades. Distillation columns are inherently nonlinear, and thus to have an accurate and robust performance, the fault detection methods should be based on nonlinear dynamic methods. The paper presents a robust data-driven fault detection approach for realistic tray upsets in the distillation column. The detection of tray faults in the distillation column is conducted by Nonlinear AutoRegressive with eXogenous Input (NARX) network with Tapped Delay Lines (TDL). Aspen Plus® Dynamic simulation has been used to generate normal and faulty datasets. The study shows that the proposed method can be used for the detection of tray faults in distillation column for dynamic process monitoring. The performance of the proposed method has been evaluated by the Missed Detection Rate (MDR) and the Detection Delay (DD).

scholarly journals PWM-VSI Diagnostic and Reconfiguration Method Based on Fuzzy Logic Approach for SSTPI-Fed IM Drives under IGBT OCFs

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mohamed Ali Zdiri ◽  
Mohsen Ben Ammar ◽  
Fatma Ben Salem ◽  
Hsan Hadj Abdallah
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Diagnostic Method ◽  
Operating Conditions ◽  
Drive System ◽  
Diagnostic Methods ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Fault Detection And Identification ◽  
Fuzzy Logic Technique

Due to the importance of the drive system reliability, several diagnostic methods have been investigated for the SSTPI-IM association in the literature. Based on the normalized currents and the current vector slope, this paper investigates a fuzzy diagnostic method for this association. The fuzzy logic technique is appealed in order to process the diagnosis variable symptoms and the faulty IGBT information. Indeed, the design, inputs, and rules of the fuzzy logic are distinct compared with the other existing diagnostic methods. The proposed fuzzy diagnostic method allows the best efficient detection and identification of the single and phase OCF of the SSTPI-IM association. Accordingly, after the fault detection and identification using this proposed FLC diagnostic method, a reconfiguration step of IGBT OCFs must be applied in order to compensate for these faults and ensure the drive system continuity. This reconfiguration is based on the change of the SSTPI-IM topology to the FSTPI-IM topology by activating or deactivating the used relays. Several simulation results utilizing a direct RFOC controlled SSTPI-IM drive system are investigated, showing the fuzzy diagnostic and reconfiguration methods’ performances, their robustness, and their fast fault detection during distinct operating conditions.

Fault Detection and Lubricant Health Monitoring for Slow Bearings in Extreme Operating Conditions

2005 ◽  
Author(s):  
Pattada Kallappa ◽  
Carl Byington ◽  
Bryan Donovan
Keyword(s):  
Fault Detection ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Vibration Monitoring ◽  
Time To Failure ◽  
Grease Lubrication ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
System Data ◽  
Actuation Systems

Slow rotating bearings are an integral part of aerospace and turbomachinery actuation systems. These actuation systems may be driven by electric, hydraulic or fueldraulic power and often operate under high loads and extreme temperatures. This makes these actuation systems and their slow rotating bearings highly susceptible to degradation and failure. Vibration monitoring techniques are not applicable to the PHM of these bearings, because their slow speeds are unable to produce a measureable vibration signature. Furthermore, the slow bearings are sealed and use grease lubrication, thus eliminating traditional oil debris monitoring. To address these problems, Impact Technologies, LLC has developed a PHM system that relies on system identification and uses available control system data and sensor measurements. This PHM system consists of algorithms and models that perform fault detection and identification for the bearings and its actuation train components like valves, pumps, motors, gears and bearings. The PHM process is divided into two stages — diagnostics and prognostics. Diagnostics is the process of detecting and isolating faults, while prognostics is the process of predicting remaining useful life (RUL) or time to failure. The authors demonstrate the PHM system through simulation on a dynamic model that is representative of hydraulic-mechanical actuation systems used in new and existing manned aircrafts, UAVs and Short Take-off and Vertical Landing aircrafts.

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