Aircraft air data system fault detection and reconstruction scheme design

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ugur Kilic ◽  
Gulay Unal
Keyword(s):  
Fault Detection ◽  
False Alarm ◽  
Fuzzy Inference ◽  
Data System ◽  
Sensor Data ◽  
Static System ◽  
Flight Crew ◽  
Reconstruction Process ◽  
Inference System ◽  
Content Type

Purpose The purpose of this study is to detect and reconstruct a fault in pitot probe and static ports, which are components of the air data system in commercial aircrafts, without false alarm and no need for pitot-static measurements. In this way, flight crew will be prevented from flying according to incorrect data and aircraft accidents that may occur will be prevented. Design/methodology/approach Real flight data collected from a local airline was used to design the relevant system. Correlation analysis was performed to select the data related to the airspeed and altitude. Fault detection and reconstruction were carried out by using adaptive neural fuzzy inference system and artificial neural networks, which are machine learning methods. MATLAB software was used for all the calculations. Findings No false alarm was detected when the fault test following the fault modeling was carried out at 0–2 s range by filtering the residual signal. When the fault was detected, fault reconstruction process was initiated so that system output could be achieved according to estimated sensor data. Practical implications The presented alternative analytical redundant airspeed and altitude calculation scheme could be used when the pitot-static system contains any fault condition. Originality/value Instead of using the methods based on hardware redundancy, the authors designed a new system within the scope of this study. Fault situations that may occur in pitot probes and static ports are modeled and different fault scenarios that can be encountered in all flight phases have been examined.

Sensor fault detection and reconstruction system for commercial aircrafts

2021 ◽  
pp. 1-17
Author(s):  
U. Kilic ◽  
G. Unal
Keyword(s):  
Fault Detection ◽  
False Alarm ◽  
Fuzzy Inference ◽  
Angle Of Attack ◽  
Sensor Data ◽  
Reconstruction Process ◽  
Inference System ◽  
Sensor Fault Detection ◽  
System Output ◽  
Commercial Aircrafts

Abstract The aim morphing of this study is to detect and reconstruct a fault in angle-of-attack sensor and pitot probes that are components in commercial aircrafts, without false alarm and no need for additional measurements. Real flight data collected from a local airline was used to design the relevant system. Correlation analysis was performed to select the data related to the angle-of-attack and airspeed. Fault detection and reconstruction were carried out by using Adaptive Neural Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), which are machine-learning methods. No false alarm was detected when the fault test following the fault modeling was carried out at 0–1 s range by filtering the residual signal. When the fault was detected, fault reconstruction process was initiated so that system output could be achieved according to estimated sensor data. Instead of using the methods based on hardware redundancy, we designed a new system within the scope of this study.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

2017 ◽  
Vol 10 (2) ◽  
pp. 166-182 ◽  
Author(s):  
Shabia Shabir Khan ◽  
S.M.K. Quadri
Keyword(s):  
Pancreatic Cancer ◽  
Fuzzy Inference System ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Patient Survival ◽  
Data Partitioning ◽  
Structure Identification ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

Adaptive neuro-fuzzy fusion of multi-sensor data for monitoring of CNC machining

Sensor Review ◽  
2017 ◽  
Vol 37 (1) ◽  
pp. 78-81 ◽  
Author(s):  
Srdjan Jovic ◽  
Obrad Anicic ◽  
Milivoje Jovanovic
Keyword(s):  
Selection Process ◽  
Spindle Speed ◽  
Cnc Machining ◽  
Numerical Control ◽  
Periodic Component ◽  
Sensor Data ◽  
Depth Of Cut ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations. Design/methodology/approach Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors. Findings There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component. Originality/value Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.

Technology intervention for preventing COVID-19 outbreak

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prateek Pandey ◽  
Ratnesh Litoriya
Keyword(s):  
Fuzzy Inference ◽  
Inference System ◽  
Content Type ◽  
Research Article ◽  
The People ◽  
Entry Points ◽  
The World ◽  
Technology Intervention ◽  
History Of ◽  
Alternative Routes

PurposeThe purpose for writing this article is derived from the misery and chaos prevalent in the world due to the coronavirus pandemic – since late 2019 and still continuing as of December 2020.Design/methodology/approachA blockchain-based solution to verify the country visit trail and disease and treatment history of the passengers who arrive at the immigration counters located at various national borders and entry points is proposed. A fuzzy inference based suspect identifier system is also presented in this article that could be utilized to make further decisions based on the degree of suspicion observed on a particular passenger.FindingsThis paper attempted to put forth a blockchain-based system which consumes the healthcare and visit trail summary of a passenger (appearing for an interview before an immigration officer) and forwards it to a fuzzy inference system to reach to a conclusion that the passenger should be advised to self-quarantine, detained, or should be allowed to enter. Such a system would help to make correct decisions at the immigration counters to check pandemic diseases, like COVID-19, right at the entry points.Research limitations/implicationsThe implications of this work are manifold. First, the proposed framework works independent of the type of pandemic and is a readymade tool to check the spread of disease through infected human carriers. Second, the proposed framework will keep the mortality rates under check, which would give ample time for the authorities to save the lives of the people with co-morbidities and age vulnerabilities (Vichitvanichphong et al., 2018). Third, it is a general phenomenon to restrict the flights from the country where the first few cases of infection are discovered; however, the infected person, at the same time, might travel through alternative routes. The blockchain-enabled proposed framework ensures the detection of such cases at no other cost. Finally, the solution may appear costly in the first place, but it has the potential to hold back the revenue of the countries that would otherwise be spent on reactive measures.Originality/valueAs of now no other study or research article provides the solution to the biggest problem persists in the world in this way. The contribution is original and worth applying.

Failure mode, effects and criticality analysis improvement by using new criticality assessment and prioritization based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Chakhrit ◽  
Mohammed Chennoufi
Keyword(s):  
Failure Mode ◽  
Conventional Method ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Fuzzy Inference ◽  
Decision Makers ◽  
Inference System ◽  
Content Type ◽  
Criticality Analysis

Purpose This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios. Design/methodology/approach To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method. Findings This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method. Originality/value This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

Modeling and predict environmental indicators for land leveling using adaptive neuro-fuzzy inference system (ANFIS), and regression

2018 ◽  
Vol 12 (4) ◽  
pp. 484-506 ◽  
Author(s):  
Farhad Mirzaei ◽  
Mahmoud Delavar ◽  
Isham Alzoubi ◽  
Babak Nadjar Arrabi
Keyword(s):  
Energy Consumption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Environmental Indicators ◽  
Inference System ◽  
Content Type ◽  
Bee Colony ◽  
Fill Volume ◽  
Land Leveling ◽  
Neural Fuzzy

PurposeThe purpose of this paper is to develop three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters.Design/methodology/approachThis paper develops three methods including artificial bee colony algorithm (ABC-ANN), regression and adaptive neural fuzzy inference system (ANFIS) to predict the environmental indicators for land leveling and to analysis the sensitivity of these parameters. So, several soil properties such as soil, cut/fill volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index in energy consumption were investigated. A total of 90 samples were collected from three land areas with the selected grid size of (20 m × 20 m). Acquired data were used to develop accurate models for labor, energy (LE), fuel energy (FE), total machinery cost (TMC) and total machinery energy (TM).FindingsBy applying the three mentioned analyzing methods, the results of regression showed that, only three parameters of sand per cent, slope and soil, cut/fill volume had significant effects on energy consumption. All developed models (Regression, ANFIS and ABC-ANN) had satisfactory performance in predicting aforementioned parameters in various field conditions. The adaptive neural fuzzy inference system (ANFIS) has the most capability in prediction according to least RMSE and the highestR2value of 0.0143, 0.9990 for LE. The ABC-ANN has the most capability in prediction of the environmental and energy parameters with the least RMSE and the highestR2with the related values for TMC, FE and TME (0.0248, 0.9972), (0.0322, 0.9987) and (0.0161, 0.9994), respectively.Originality/valueAs land leveling with machines requires considerable amount of energy, optimizing energy consumption in land leveling operation is of a great importance. So, three approaches comprising: ABC-ANN, ANFIS as powerful and intensive methods and regression as a fast and simplex model have been tested and surveyed to predict the environmental indicators for land leveling and determine the best method. Hitherto, only a limited number of studies associated with energy consumption in land leveling have been done. In mentioned studies, energy was a function of the volume of excavation (cut/fill volume). Therefore, in this research, energy and cost of land leveling are functions of all the properties of the land including slope, coefficient of swelling, density of the soil, soil moisture, special weight and swelling index which will be thoroughly mentioned and discussed. In fact, predicting minimum cost of land leveling for field irrigation according to the field properties is the main goal of this research which is in direct relation with environment and weather pollution.

Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system (ANFIS) controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lie Yu ◽  
Lei Ding ◽  
Fangli Yu ◽  
Jianbin Zheng ◽  
Yukang Tian
Keyword(s):  
Tracking Control ◽  
Fuzzy Inference ◽  
Sinusoidal Signal ◽  
Inference System ◽  
Content Type ◽  
Signal Tracking ◽  
Neuro Fuzzy ◽  
Actual Force ◽  
Force Tracking ◽  
Electrohydraulic Servo System

PurposeThe purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.Design/methodology/approachTwo types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.FindingsThe models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.Originality/valueThis paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
İlker Gölcük
Keyword(s):  
Failure Modes ◽  
Fuzzy Inference ◽  
Real Life ◽  
Effect Analysis ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
Interval Type

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

Demand forecasting in the beauty industry using fuzzy inference systems

2020 ◽  
Vol 15 (4) ◽  
pp. 1389-1417
Author(s):  
Ricardo Felicio Souza ◽  
Peter Wanke ◽  
Henrique Correa
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Theory And Practice ◽  
Forecast Errors ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Content Type ◽  
Forecasting Methods ◽  
Inference Systems

Purpose This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company. Design/methodology/approach The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based). Findings The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods. Originality/value Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

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