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The purpose of this paper is to present the irregular deviation examination of flight control surfaces and the potential problem diagnosis of irregular deviations for the jet transport aircraft. A four-jet transport aircraft at transonic flight in cruise phase is the study case of the present article.

The standard lift-to-drag ratio (L/D) and flight dynamic models are established through flight data mining and the fuzzy logic modeling technique based on the flight data of quick access recorder available in the Flight Operations Quality Assurance (FOQA) program of the airlines. The irregular deviations of flight control surfaces are examined by the standard L/D model-predicted results through sensitivity analysis. The contribution values in L/D deficiency are predicted by the deviations and the L/D derivatives of all influencing variables in Taylor series expansion. The potential problems due to irregular deviations can be excavated by the flight dynamic models through the analysis of in-flight stability and controllability.

The magnitude of stabilizer angle to the deficiency of L/D is the largest among the four control surfaces and elevator is the second one through the judgment of contribution values in L/D deficiency. The stabilizer has irregular deviations with obvious endplay problems of jackscrew, as found in the present study. The stabilizer is suggested to have the unscheduled maintenance for the flight control rigging.

The specific transport aircraft of the standard L/D model should be the best one in L/D performance among all transport aircraft in the fleet of the airlines. The present method is a new concept to monitor the irregular deviation of flight control surface. The study case of the four-jet transport aircraft at transonic flight in cruise phase is illustrated as the standard L/D mode. The required flight data of monitored flight is requested to eliminate the biases through compatibility checks. The flight data of study case in the present study is also illustrated as monitored flight data.

To diagnose the irregular deviations of flight control surface deflected angles with contributing to the L/D deficiency estimation is an innovation to improve the flight data analysis of FOQA program for airlines. If the irregular deviation problems of control surfaces can be fixed after rigging in maintenance, the goal of flight safety and aviation fuel saving will be achieved.

The flight control surface rigging of unscheduled maintenance is not expected to coincide with an airline’s peak season or unavailable space in hangar. The optimal time of unscheduled maintenance for the flight control rigging will be easily decided through the correlations between excessive fuel cost and flight safety.

This method can be used to assist airlines to monitor irregular angular positions of flight control surfaces as a complementary tool for management to improve aviation safety, operation and operational efficiency.

The purpose of this paper is to present a quick inspection method based on the post-flight data to examine static aeroelastic behavior for transport aircraft subjected to instantaneous high g-loads.

In the present study, the numerical approach of static aeroelasticity and two verified cases will be presented. The non-linear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling of artificial intelligence techniques based on post-flight data. The first and second derivatives of flight dynamic and static aeroelastic behaviors, respectively, are then estimated by using these aerodynamic models.

The flight dynamic and static aeroelastic behaviors with instantaneous high g-load for the two transports will be analyzed and make a comparison study. The circumstance of turbulence encounter of the new twin-jet is much serious than that of four-jet transport aircraft, but the characteristic of stability and controllability for the new twin-jet is better than those of the four-jet transport aircraft; the new twin-jet transport is also shown to have very small aeroelastic effects. The static aeroelastic behaviors for the two different types can be assessed by using this method.

As the present study uses the flight data stored in a quick access recorder, an intrusive structural inspection of the post-flight can be avoided. A tentative conclusion is to prove that this method can be adapted to examine the static aeroelastic effects for transport aircraft of different weights, different sizes and different service years in tracking static aeroelastic behavior of existing different types of aircraft. In future research, one can consider to have more issues of other types of aircraft with high composite structure weight.

This method can be used to assist airlines to monitor the variations of flight dynamic and static aeroelastic behaviors as a complementary tool for management to improve aviation safety, operation and operational efficiency.

The purpose of this paper is to present a comparative study of flight circumstances, dynamic stability characteristics and controllability for two transport aircraft in severe atmospheric turbulence at transonic cruise flight for the purpose to obtain the prevention concepts of injuries to passengers and crew members for pilot training in International Air Transport Association (IATA) – Loss of Control In-flight (LOC-I) program.

A twin-jet and a four-jet transport aircraft encountering severe atmospheric turbulence are the study cases for this paper. The nonlinear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling technique based on the flight data of flight data recorder. This method can be adopted to examine the influence of horizontal wind shear and crosswind on loss of control, dynamic stability characteristics and controllability for transport aircraft in different weights and different sizes in tracking aviation safety of existing different types of aircraft.

The horizontal wind shear or crosswind before the turbulence encounter will easily induce rolling motion and then initiate the sudden plunging motion during the turbulence encounter. The roll rate will increase the oscillatory rolling motion during plunging motion, if the rolling damping is insufficient. The drop-off altitude will be enlarged by the oscillatory rolling motion during the sudden plunging motion.

A lack of the measurement data of vertical wind speed sensor on board to verify the estimated values of damping term is one of the research limitations for this study. The fact or condition of being severe in sudden plunging motion can be judged through the analysis of oscillatory derivatives with both dynamic stability and damping terms.

The roll rate will increase the oscillatory rolling motion during plunging motion, if the rolling damping is insufficient. The drop-off altitude will be enlarged by the oscillatory rolling motion during the sudden plunging motion. The horizontal wind shear or crosswind before the turbulence encounter will easily induce rolling motion and then initiated the sudden plunging motion during the turbulence encounter. If the drift angle is large, to turn off the autopilot of yaw control first and stabilize the rudder by the pedal. When passing through the atmosphere turbulence area, the pilots do not need to amend the heading angle urgently.

The flight safety prevention in avoidance of injuries for passengers and cabin crews is essential for the airlines. The horizontal wind shear or crosswind before the turbulence encounter will easily induce rolling motion and then initiated the sudden plunging motion during the turbulence encounter.

The flight safety prevention in avoidance of injuries for passengers and cabin crews is essential. The present assessment method is an innovation to examine the loss of control problems of aviation safety and promote the understanding of aerodynamic responses of the jet transport aircraft. It is expected to provide a valuable lecture for the international training courses for IATA – LOC-I program after this paper is being published.

This study aims to present a diagnosis method to inspect the structure health for aging transport aircraft based on the postflight data in severe clear-air turbulence at transonic flight. The purpose of this method development is to assist certificate holder of aircraft maintenance factory as a complementary tool for the structural maintenance program to ensure that the transport aircraft fits airworthiness standards.

In this study, the numerical approach to analyze the characteristics of flight dynamic and static aeroelasticity for two four-jet transport aircraft will be presented. One of these two four-jet transport aircraft is an aging one. Another one is used to demonstrate the order of magnitude of the static aeroelastic behaviors. The nonlinear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling technique based on postflight data. The first and second derivatives of flight dynamic and static aeroelastic behaviors, respectively, are then estimated by using these aerodynamic models.

Although the highest dynamic pressure of aging aircraft is lower, the highest absolute value of static aeroelastic effects response to the wing of aging aircraft is about 3.05 times larger than normal one; the magnitude variations of angles of attack are similar for both aircrafts; the highest absolute value of the static aeroelastic effects response to the empennage of aging aircraft is about 29.67 times larger than normal one in severe clear-air turbulence. The stabilizer of aging aircraft has irregular deviations with obvious jackscrew assembly problems, as found in this study.

A lack of the measurement data of vertical wind speed sensor on board to verify the estimated values of damping term is one of the research limitations of this study. This research involved potential problem monitoring of structure health for transport aircraft in different weights, different sizes and different service years. In the future research, one can consider more structural integrity issues for other types of aircraft.

It can be realized from this study that the structure of aging transport aircraft may have potential safety threat. Therefore, when the airline managed aging transport aircraft, it ought to be conducted comprehensive and in-depth inspections to reduce such safety risks and establish a complete set of safety early warning measures to deal with the potential problem of aircraft aging.

It can be realized that the structure of aging transport aircraft has potential safety threat. The airline managed aging transport aircraft; it should conduct comprehensive and in-depth inspections to reduce safety risks and establish a complete set of safety early warning measures.

This method can be used to assist airlines to monitor aging transport aircraft as a complementary tool of structural maintenance program to improve aviation safety, operation and operational efficiency.

The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values.

A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts.

This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity.

Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study.

The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities.

This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.

The purpose of this paper is to study the consumer opinion towards the low-cost airlines or low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in revealing current customers’ satisfaction level towards low-cost airlines.

About 10,895 tweets (data collected for two and a half months) are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical techniques are used to segment the customers’ opinion.

The results with two different sentiment algorithms show that there is more positive than negative polarity across the different algorithms. Clustering results show that both K-Means and spherical K-Means algorithms delivered similar results and the four main topics that are discussed by the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and delays and post-booking management.

Gaining knowledge of customer sentiments as well as improvements on the four main topics discussed in this study, i.e. customer service, LCCs tickets promotions, flight cancellations or delays and post-booking management will help LCCs to attract more customers and generate more profits.

This paper provides useful insights on customers’ sentiments and opinions towards LCCs by utilizing social media information.

The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts.

The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts.

At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice.

There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

The purpose of this paper is to develop a customer online behaviour analysis tool, segment high-value customers, analyse their online purchasing behaviour and predict their next purchases from an online air travel corporation.

An operations review of the customer online shopping process of an online travel agency (OTA) is conducted. A customer online shopping behaviour analysis tool is developed. The tool integrates competitors’ pricing data mining, customer segmentation and predictive analysis. The impacts of competitors’ price changes on customer purchasing decisions regarding the OTA’s products are evaluated. The integrated model for mining pricing data, identifying potential customers and predicting their next purchases helps the OTA recommend tailored product packages to its individual customers with reference to their travel patterns.

In the customer segmentation analysis, 110,840 customers are identified and segmented based on their purchasing behaviour. The relationship between the purchasing behaviour in an OTA and the price changes of different OTAs are analysed. There is a significant relationship between the flight duration time and the purchase lead time. The next travel destinations of segmented high-value customers are predicted with reference to their travel patterns and the significance of the relationships between destination pairs.

The developed model contributes to pricing evaluation, customer segmentation and package customization for online customers.

This study provides novel method and insights into customer behaviour towards OTAs through an integrated model of customer segmentation, customer behaviour and prediction analysis.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

Research on how to use social media data to measure and evaluate service quality is still limited. To fill the research gap in the literature, the purpose of this paper is to open a new avenue for future work to measure the service quality in the service industry by developing a new analytical approach of using social media analytics to evaluate service quality.

This paper uses social media data to measure the service quality of the airline industry with the SERVQUAL metrics. A novel benchmark data set was created for each SERVQUAL metric. The data set was analyzed through text mining and sentiment analysis.

By comparing the results from social media with official service quality report from the Department of Transportation, the authors found that the proposed service quality metrics from social media are valid and can be used to estimate the service quality.

This paper presents service quality metrics and a methodology that can be easily adopted by other businesses to assess service quality. This study also provides guidance and suggestions to help businesses understand how to collect and analyze social media data for the purpose of evaluating service quality.

This paper offers a novel methodology that uses text mining and sentiment analysis to help the airline industry assess its service quality.