Residual Life Estimation of Humidity Sensor DHT11 Using Artificial Neural Networks

Electronic systems have become an integral part of our daily lives. From toy to radar, system is dependent on electronics. The health conditions of humidity sensor need to be monitored regularly. Temperature can be taken as a quality parameter for electronics systems, which work under variable conditions. Using various environmental testing techniques, the performance of DHT11 has been analysed. The failure of humidity sensor has been detected using accelerated life testing, and an expert system is modelled using various artificial intelligence techniques (i.e., Artificial Neural Network, Fuzzy Inference System, and Adaptive Neuro-Fuzzy Inference System). A comparison has been made between the response of actual and prediction techniques, which enable us to choose the best technique on the basis of minimum error and maximum accuracy. ANFIS is proven to be the best technique with minimum error for developing intelligent models.

[1]  Gordon Hayward,et al.  Fuzzy logic applications. , 2003, The Analyst.

[2]  P. Venet,et al.  Realization of a smart electrolytic capacitor circuit , 2002 .

[3]  M. Lefik.  Some aspects of application of artificial neural network for numerical modeling in civil engineering , 2013 .

[4]  Shuting Lei,et al.  Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations , 2004 .

[5]  S. Parler.  Thermal modeling of aluminum electrolytic capacitors , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[6]  Cherry Bhargava,et al.  Reliability Prediction of Soil Humidity Sensor using Parts Count Analysis Method , 2016 .

[7]  S.M. Virk,et al.  Fault Prediction Using Artificial Neural Network and Fuzzy Logic , 2008, 2008 Seventh Mexican International Conference on Artificial Intelligence.

[8]  R. Kötz,et al.  Temperature behavior and impedance fundamentals of supercapacitors , 2006 .

[9]  D. Dinakaran,et al.  Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool , 2017 .

[10]  Ronald T. Anderson,et al.  Reliability based quality (RBQ) technique for evaluating the degradation of reliability during manufacturing , 1979 .

[11]  K. Mohandas,et al.  Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools , 2015, J. Intell. Manuf..

[12]  Tong Zhang,et al.  Humidity sensors based on Li-loaded nanoporous polymers , 2014 .

[13]  Özgür Kisi,et al.  Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques , 2018, Neural Computing and Applications.

[14]  Sajan Seth.  MExS A Fuzzy Rule Based Medical Expert System To Diagnose The Diseases , 2014 .

[15]  Aris Christou,et al.  Lifetime Estimation of Insulated Gate Bipolar Transistor Modules Using Two-Step Bayesian Estimation , 2017, IEEE Transactions on Device and Materials Reliability.

[16]  Joseph C. Chen,et al.  Development of a fuzzy-nets-based surface roughness prediction system in turning operations , 2007, Comput. Ind. Eng..

[17]  Gunasekaran Manogaran,et al.  RETRACTED ARTICLE: Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System , 2017, Multimedia Tools and Applications.

[18]  Xiaoh Wang.  Intelligent Modeling and Predicting Surface Roughness in End Milling , 2009, 2009 Fifth International Conference on Natural Computation.

[19]  Pouran Moradipour,et al.  A novel fabrication of sensor using ZnO-Al2O3 ceramic nanofibers to simultaneously detect catechol and hydroquinone , 2018 .

[20]  Michael Pecht,et al.  Electronic part life cycle concepts and obsolescence forecasting , 2000 .

[21]  M. M. Siddiqui,et al.  Residual lifetime distribution and its applications , 1994 .

[22]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[23]  H. H. Huston,et al.  Reliability defect detection and screening during processing-theory and implementation , 1992, 30th Annual Proceedings Reliability Physics 1992.