One file ITECH5500 name consists of literature synthesis matrix report done by me on predictive...

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One file ITECH5500 name consists of literature synthesis matrix report done by me on predictive analysis on AI. The other file consists of guidelines for research proposal which is to be done on same topic as synthesis matrix words around 2500-300 (excludingthe title, abstract, table of contents and references/bibliography) APA style.

ITECH 5500 Professional Research and Communication Literature Synthesis ROHIT PRADIP JOSHI 30363485 LITERATURE SYNTHESIS 1 Table of Contents Introduction ................................................................................................................................ 2 A. Interim defined research proposal ......................................................................................... 3 B. Synthesis-matrix .................................................................................................................... 4 Conclusion ................................................................................................................................. 7 References .................................................................................................................................. 8 LITERATURE SYNTHESIS 2 Introduction This given defined report is built on professional & communication research on a specific topic. This topic is related to “Predictive airlines maintenance through Artificial intelligence”. In this report, I am going to research through the usage of various journals, and an interim proposal is being made on this topic considering several literatures. This report also airs a matrix in which summarised ideas has been bestowed from the article about this topic. LITERATURE SYNTHESIS 3 A. Interim defined research proposal Defined topic: Predictive airlines maintenance through Artificial intelligence Here, I analysed six different kinds of literature based on Predictive airlines safety maintenance through Artificial intelligence. Thus, this interim research indicates deep and analysed research on given topic using six journals. From this, I discovered that befitting usage of AI can deliver maximum safety during aeroplane landing. Through help of this, it correspondingly gets to distinguish that AI technologies like machine learning are also immensely helpful which provide a prediction of the bad weather condition for safe landing (Iyer, 2019). Ideas which have been conferred through this literature, indicate that certain AI-based system supply the usage of sensors which provides sensing of an airline during downpour. Most significant features that have been provided through these journals’ states, for safe autonomous landing there is a requirement of robots to measure any uncertainty on Airlines during aeroplane landing. Not only landing but also flying requires safety for an Airlines that has been provided using AI-based tools and technologies. This journal can illustrate that this study is not enough for evaluating Airlines safety maintenance using Artificial intelligence. Thus, it required more research to get a detailed idea towards it through more articles (Ganesh, et. al., 2018). LITERATURE SYNTHESIS 4 B. Synthesis-matrix According to Masson, et. al., 2017 According to Baomar & Bentley, 2017 According to the Ayra, et, al., 2019 Main Idea 1 Airlines required more safety during the time of landing for which autonomous robots based on AI has been allowed for the reason of safety issues. During the severe conditions of weather, it is exceedingly difficult to manage the Airlines for safety. Thus, safety purpose Artificial intelligence has been employed within a respectful way. For managing the Airlines, it is needed to provide the usage of artificial intelligence- based Bayesian network as per which safety is highly secured manner. Main Idea 2 With the help of artificial-based intelligence, it is used to provide the hazard identification technique like HAZOP-UML by which any risk towards the ladling of airport. Such through usage of AI, mitigation of this issue has been taking place. With the help of AI, neural network-based AI has been employed which provides detail mapping of the Airlines so that safety has been taking place during its landing. Excursion of a Airlines during the time of landing needed aviation-based safety. Due to this AI used to provide safety, respectively. Thus, this is the reason because of which it required a proper usage of AI system for Airlines maintenance. Main Idea 3 With the usage of Artificial-based intelligence, certain monitoring is used to be provided through which landing becomes safe. Intelligence autopilot- based system is one of the most important ways which safety on the airport Airlines has been provided most regularly. It indicates that during the time of management certain components has been taking in use which provides flight simulator usage, interface usage, database usage, flight programming and ANN. Run overruns landing is used to be managed by the usage of Artificial intelligence. Such through usage of AI mitigation of these issues has been taking place. Main Idea 4 This article also represents the safety monitor which has been programmed in such a way that it provides With the help of defined neural network based on artificial intelligence, it provides prediction like aeroplane is going in a Using this Bayesian network management of the landing has been taking place where proper LITERATURE SYNTHESIS 5 easiest way of landing through airport. correct direction or not. If not, then it provides correct mapping alert notification. safety is being provided (Zhang, 2019). Main Idea 5 Robotics intelligence has been used by which safety during landing is being provided in efficient manner. For the safety of air Airlines, it is needed to make the use of AI-based technology by which autonomous landing is going to be provided within a specific manner. Such safety towards it going to be enhanced within respective manner. With the artificial intelligence prediction towards the landing has been provided by which safety from an airport Airlines is going to be reduced within a specified manner. Main Idea 6 This is used to indicate that through the usage of light measuring within airport provides active monitoring using the Artificial intelligence robot. Most important and unique idea which we get from this is that autonomous controlling- based system is just to provide simplicity within the landing so that safety is also provided, respectively. One of main idea that has been provided from this report indicates that BN structure provides a mathematical measurement of distance between

itech-5500-professional-research-and-communication-new-1-tvc4ibox.pdf research-proposal-requirements-5500-unkfxieq.pdf

Neha · Accepted Answer

Automated Failure Diagnosis in Aviation Maintenance Using eXplainable Artificial Intelligence (XAI)
ABSTRACT 
When we have incomplete or incorrect repair card and typically when it is used in the aviation maintenance to report the failures that it can result in incorrect maintenance and it can create very hard situations for analysing the maintenance data. There are different reasons to have incomplete reporting. Firstly, the information is generally unknown when it he is filled in the maintenance data by the crew. The findings which are present on the repair cards are filled in the form of free form text which makes it difficult to automatically understand any findings. The automatic access failure description can result in more complete and consistent repair cards this technique can help us to improve the efficiency of finding out as the failure diagnose can help to add more information which would not be present at the disposal time for the maintenance crew. This research can help to utilise our data driven approach which combines the usage and materials of the data. This model is generally based on the artificial intelligence to understand the physics component or subsystem completely you propose intelligence methodology and field diagnosis explainability is added to the model to provide more transparency to the access diagnosis. 
INTRODUCTION 
For maintaining the aviation our repair card is used to report about any anomaly or failure register it for the maintenance of management system. If there is any incomplete or incorrect repair card, then it can result in incorrect maintenance of the aviation system and it can create difficult scenario for analysing the maintenance data. Example from the practice is main gearbox of the helicopter and its removal to leakage during inspection of 500 hours. The maintenance crew explained the complaint as a defect and 500 hours. The overhaul was carried out by the component shop when a small repair was capable of solving the whole problem.
The overhaul was unforeseen and there was no spare available for the gearbox which resulted in grounding the helicopter. These consequences of getting incomplete repair card can increase the maintenance cost and decrease the availability. This example is not unique in this sector and it is very common and there are different reasons behind this. Firstly the information is not known to the new member while filling the information in the repair card and the findings on the repair card are filled in a free form text which can result to add incorrect information or incomplete information into the repair car consequence of having incomplete or incorrect repair card is that they cannot be used for practically analysing the data because it is not possible to automatically interpret the results (Shepherd, W. T., Layton, C. F., & Gramopadhye, A. K). 
Therefore, there is a need of generating a model which has capability of diagnosing the failure automatically on the basis of usage data. Automatically access feature description can help you get consistent and complete repair cars this model can help to increase the efficiency of troubleshooting the issue as failure diagnosis can add new information which would not be possible at the disposal of maintenance crew.  
DATA PRE-PROCESSING 
Training any model, it is compulsory to be processed the data so that we can implement machine learning algorithm over it. This results in new questions like which data, format of the data and how much data is required to have proper training of the model. The whole model is based on the historic maintenance and call the data which is stored as the sensor data from historic flights. In the research work we will use maintenance data to label the failures of the aviation system. Usage data will be added to find out the historic usage of any component to perform assessment over the diagnosis. In this section we will discuss about the algorithm prerequisites, type of data and all the required variables. The requirement will be different for each system and for each component (Strauch, B., & Sandler, C. E). 
Algorithm prerequisites 
There is no specific requirement about the data set which will be applied to the machine learning algorithm. One of the authors mentioned in 2005 that there should always be rules of thumb generalised format from any specific case. The sample size of the data will depend over the situation. The following are general guideline switch will remain same for each type of situation about the data. 
· The number of features can greatly influence the performance of algorithm. Therefore, it is important to understand number of features which needs to have close to the optimal amount. 
· The optimal amount of the features which are related with the sample size is directly dependent over the algorithm and feature output distribution. Mister also presents the relation between the number of features, number of samples and error rate for different algorithms. 
· It is preferable to use balanced labelled data set in which each class has same number of samples instead of using overfitting over the class which can contain more examples.

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