The requirements for automation systems based on Boeing 737 MAX crashes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Seref Demirci
Keyword(s):  
Federal Aviation Administration ◽  
Lessons Learned ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Automation System ◽  
Content Type ◽  
Aerodynamic Model ◽  
Automation Systems ◽  
Detection And Diagnosis ◽  
Certification Requirements

Purpose This paper aims to show the current situation and additional requirements for the aircraft automation systems based on the lessons learned from the two 737 MAX crashes. Design/methodology/approach In this study, the Swiss cheese model was used to find the real root causes of the 737 MAX accidents. Then, the results have been compared with the actions taken by the manufacturers and authorities. Based on the comparison, the necessary improvements to prevent such accidents are defined. Regarding the faulty sensor that forms the accidents, a synthetic sensor was developed using an aerodynamic model. Findings It has been proven that the safety-critical automation systems should not be designed by relying on a single set of sensor data. Automation levels should be defined in a standard way. Depending on the defined automation level, the system must be designed as either fail-safe or fail-operational system. When designing backup systems, it should be decided by looking at not only whether it has power but also the accuracy of the incoming signals. Practical implications Aviation certification requirements related to automation systems need to be revised and improved. With this context, it was revealed that the certification processes for automation systems should be re-evaluated and updated by aviation authorities, especially Federal Aviation Administration and European Union Aviation Safety Agency. Originality/value Task sharing between automation system and pilot based on the classification of automation levels and determining certification requirements accordingly has been brought to the agenda. A synthetic Angle of Attack sensor was developed by using an aerodynamic model for fault detection and diagnosis.

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

scholarly journals Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yimin Chen ◽  
Jin Wen
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Field Evaluation ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Component Level ◽  
Automation Systems ◽  
Tightly Coupled ◽  
Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

Towards a new library system

2015 ◽  
Vol 36 (1/2) ◽  
pp. 2-11 ◽  
Author(s):  
Jarmo Saarti ◽  
Sinikka Luokkanen ◽  
Ari Ahlqvist ◽  
Lassi Lager
Keyword(s):  
Policy Making ◽  
Public Libraries ◽  
Automation System ◽  
Ministry Of Education ◽  
Content Type ◽  
Joint Project ◽  
Library Automation ◽  
Web Technologies ◽  
Library System ◽  
Automation Systems

Purpose – Finnish libraries are using different integrated library systems. Higher education libraries funded by the Ministry of Education and Culture are using the same ILS in three different installations on the same hardware. Special and public libraries are using several different systems. Many of these library systems are reaching their end-of-life phase. During the spring and summer of 2011 all the Finnish library sectors together with the National Library of Finland formed a joint committee in order to assess the feasibility of a library system entirety, possibly an open source solution that would suit the needs of all the different types of libraries. The purpose of this paper is to describe and analyse the planning for the acquisition of a new library system initiated in year 2012; the concept is to try to establish a joint system with common databases for all the libraries in all sectors willing to collaborate in this effort. Design/methodology/approach – The paper describes the evolution of the Finnish library systems and evaluates the methods used in the planning of the new library automation system. Findings – The broad model of group working was useful in policy making and committing the libraries to the joint project. Using social web-technologies were efficient in project communication and marketing. This type of semi-professional planning was not able to produce accurate specification for programming thus a need for follow-up project became evident. Research limitations/implications – The paper is based on Finnish experiences. Social implications – The paper presents a case about the usage of group working in the planning of a library automation system with an evaluation of the possibilities and restraints on this type of approach. Originality/value – The paper provides an analysis on the usability of broad group working type of approach to the policy making and planning of library automation systems.

Public library staff’s perceived value and satisfaction of an open source library system

2014 ◽  
Vol 32 (3) ◽  
pp. 390-402 ◽  
Author(s):  
Barbara Albee ◽  
Hsin-liang Chen
Keyword(s):  
Open Source ◽  
Public Library ◽  
Sampling Procedure ◽  
Automation System ◽  
Convenience Sample ◽  
Content Type ◽  
Daily Work ◽  
Library Automation ◽  
Automation Systems ◽  
Library Staff

Purpose – The purpose of this study is to examine public library staff attitudes towards an open-source library automation system in the state of Indiana. The researchers were interested in understanding the library staff’s perceptions of the value of the system in performing their job duties and improving library services. Design/methodology/approach – The researchers travelled to nine public libraries every three months to survey library staff from January to December 2010. The participants completed the surveys at the libraries and were given the option to remain anonymous. The survey consisted of six questions regarding the use of the Evergreen system for work processes and basic demographic information of the staff. There were a total of 323 survey respondents. Of the 323 respondents, 57 (17.65 per cent) used the Evergreen system in their daily work routines at the library. Findings – The primary benefits reported were: ability to check the availability of library materials at other Evergreen libraries via the shared catalogue, the Evergreen system provided more functionality than their previous library automation systems and the ability to reserve materials for patrons. Research limitations/implications – This was a convenience sample. All survey participants provided their responses voluntarily during the 12-month study period. A more comprehensive sampling procedure should be considered in the future. Originality/value – The study indicated the need for improvements in the Evergreen Indiana system. Those improvements were also relevant to other open-source integrated library systems.

Turbine Engine Modeling Using Temporal Neural Networks for Incipient Fault Detection and Diagnosis

2004 ◽  
Author(s):  
Sunil Menon ◽  
O¨nder Uluyol ◽  
Deepanker Gupta
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Turbine Engine ◽  
Echo State Networks ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Detection And Diagnosis ◽  
Incipient Fault Detection

We present a method of fault detection and diagnosis in turbine engines using temporal neural networks. Temporal neural networks allow us to represent the complete engine operating range by complementing the first-principle models which are usually restricted to takeoff and cruise phases. Because faults that are manifest only in particular phases can be detected, complete coverage leads to more accurate anomaly detection and fault diagnosis systems. The time series sensor data from the engine is collected during particular aircraft flight phases such as startup, takeoff, cruise, and shutdown. We use the echo state network to develop an incipient fault detection and diagnosis system. Echo state networks have several advantages over conventional types of temporal neural networks, including accuracy and ease of training. We demonstrate the efficacy of using the echo state networks to focus on flight phases that are difficult to model. We present results of our fault detection and diagnosis method with actual propulsion engine transient flight data.

Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ronny Francis Ribeiro Junior ◽  
Isac Antônio dos Santos Areias ◽  
Guilherme Ferreira Gomes
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Electric Motors ◽  
Content Type ◽  
Operation Conditions ◽  
Vibration Pattern ◽  
Vibration Signal Analysis ◽  
Detection And Diagnosis

Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 75
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

Sign in / Sign up

Export Citation Format

Share Document