Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired events, as there are techniques and methods that study the detection, isolation, identification and, consequently, fault diagnosis. In this work, a new methodology that uses faults emulation to obtain parameters similar to the Development and Application of Methods for Diagnosis of Actuators in Industrial Control Systems (DAMADICS) benchmark model will be developed. This methodology uses previous information from tests on sensors with and without faults to detect and classify the situation of the plant and, in the presence of faults, perform the diagnosis through a process of elimination in a hierarchical manner. In this way, the definition of residue signature is used as well as the creation of a decision tree. The whole process is carried out incorporating FDD techniques, through the Non-Linear Auto-Regressive Neural Network Model With Exogenous Inputs (NARX), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the construction of the decision tree based on the most significant residue of a certain signal, enabling the process of acquisition and formation of the signature matrix. With the procedures in this article, it is possible to demonstrate a practical and systematic method of how to emulate faults for control valves and the possibility of carrying out an analysis of the data to acquire signatures of the fault behavior. Finally, simulations resulting from the most sensitized variables for the production of residuals that is generated by neural networks are presented, which are used to obtain signatures and isolate the flaws. The process proves to be efficient in computational time and makes it easy to present a fault diagnosis strategy that can be reproduced in other processes.

[1]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[2]  B. Kannapiran,et al.  Fault Diagnosis of Pneumatic Valve with DAMADICS Simulator using ANN based Classifier Approach , 2013 .

[3]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[4]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[5]  Dimitri Lefebvre,et al.  Nerual Networks with Decision Trees for Diagnosis Issues , 2013, CSE 2013.

[6]  Venkat Venkatasubramanian,et al.  Process Fault Detection and Diagnosis: Past, Present and Future , 2001 .

[7]  Janos Gertler,et al.  ANALYTICAL REDUNDANCY METHODS IN FAULT DETECTION AND ISOLATION: Survey and Synthesis , 1992 .

[8]  Krzysztof Patan,et al.  Soft Computing Approaches to Fault Diagnosis for Dynamic Systems , 2001, Eur. J. Control.

[9]  Madhuri Jha.  ANN-DT : An Algorithm for Extraction of Decision Trees from Artificial Neural Networks , 2013 .

[10]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[11]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[12]  Yuebin Yu,et al.  A review of fault detection and diagnosis methodologies on air-handling units , 2014 .

[13]  Sánchez A. José,et al.  Improvements in failure detection of DAMADICS control valve using neural networks , 2017, 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM).

[14]  Haslinda Zabiri,et al.  Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems , 2018, Neural Computing and Applications.

[15]  Asok Ray,et al.  Sensor Fusion for Fault Detection and Classification in Distributed Physical Processes , 2014, Front. Robot. AI.

[16]  Swetha Rao,et al.  Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine , 2019, Electronics.

[17]  Jan Lunze,et al.  Sensor and actuator fault diagnosis of systems with discrete inputs and outputs , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Woohyun Kim,et al.  A review of fault detection and diagnostics methods for building systems , 2018 .

[19]  Krzysztof Patan,et al.  Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes , 2008 .

[20]  Dirk van Schrick,et al.  Remarks on Terminology in the Field of Supervision, Fault Detection and Diagnosis , 1997 .

[21]  Mingsheng Wang,et al.  On the Accuracy of Fault Diagnosis for Rolling Element Bearings Using Improved DFA and Multi-Sensor Data Fusion Method , 2020, Sensors.

[22]  Joseba Quevedo,et al.  Introduction to the DAMADICS actuator FDI benchmark study , 2006 .

[23]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[24]  Bin Hu,et al.  Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers , 2019, Sensors.

[25]  Peng Wang,et al.  An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox , 2017, Sensors.

[26]  Mateusz Kalisch,et al.  Application of selected classification schemes for fault diagnosis of actuator systems , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[27]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[28]  Wen Jiang,et al.  A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion , 2017 .

[29]  Andrzej Katunin,et al.  Faults diagnosis using self-organizing maps: A case study on the DAMADICS Benchmark problem , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[30]  D. Lefebvre,et al.  New technique for online faults diagnosis based on faulty models design: Application to DAMADICS actuator , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[31]  Plamen P. Angelov,et al.  An evolving approach to unsupervised and Real-Time fault detection in industrial processes , 2016, Expert Syst. Appl..

[32]  Haslinda Zabiri,et al.  Fault detection in distillation column using NARX neural network , 2018, Neural Computing and Applications.

[33]  Joseba Quevedo,et al.  A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors † , 2018, Sensors.

[34]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[35]  D. Devaraj,et al.  Artificial neural network approach for fault detection in rotary system , 2008, Appl. Soft Comput..

[36]  Masoud Soroush,et al.  A method of sensor fault detection and identification , 2005 .