Automatic Engine Modeling for Failure Detection

2001 ◽  
Author(s):  
A. Schrempf ◽  
L. del Re ◽  
W. Groißböck ◽  
E. Lughofer ◽  
E. P. Klement ◽  
...  
Keyword(s):  
Fault Detection ◽  
Failure Detection ◽  
Abnormal Behavior ◽  
Automatic Modeling ◽  
Plant Operation ◽  
Fast Detection ◽  
Engine Modeling ◽  
Normal Behavior ◽  
Large Production ◽  
Abnormal Plant

Abstract Fast detection of abnormal plant operation is critical for many applications. Fault detection requires some kind of comparison between actual and “normal” behavior, which implies the use of models. Exact modeling of engine systems is probably impossible and even middle-complexity models are very time-consuming. In some few cases, as for on board diagnostics, the very limited amount of cases to be treated and the usually large production volumes allow to develop models suitable to detect an abnormal behavior, but, in general, however, this approach cannot be followed. As fast detection of abnormal plant operation is often critical, alternative low-effort approaches are required. This paper presents a procedure suitable for engine fault detection based on parallel automatic modeling. It is shown that this approach yields a flexible and reliable tool for automatic modeling for this goal, while keeping the effort for the operator rather low.

scholarly journals GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Drones ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 8
Author(s):  
Elena Basan ◽  
Alexandr Basan ◽  
Alexey Nekrasov ◽  
Colin Fidge ◽  
Nikita Sushkin ◽  
...  
Keyword(s):  
Cyber Security ◽  
Detection System ◽  
Attack Detection ◽  
Abnormal Behavior ◽  
Mathematical Methods ◽  
Training Time ◽  
Detection Technology ◽  
Normal Behavior ◽  
Leibler Divergence ◽  
Aerial Vehicle

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

scholarly journals Analysis Of Failure Detection And Visibility Criteria In Pantograph-catenary Interaction

2020 ◽  
pp. 127-135 ◽  
Author(s):  
Sakir Parlakyıldız ◽  
Muhsin Tunay Gencoglu ◽  
Mehmet Sait Cengiz
Keyword(s):  
Fault Detection ◽  
Error Detection ◽  
Failure Detection ◽  
Detection Methods ◽  
Main Subject ◽  
Test Environment ◽  
Railway Systems ◽  
Lighting Conditions ◽  
Rail Systems ◽  
Led Luminaire

The main purpose of new studies investigating pantograph catenary interaction in electric rail systems is to detect malfunctions. In the pantograph catenary interaction studies, cameras with non-contact error detection methods are used extensively in the literature. However, none of these studies analyse lighting conditions that improve visual function for cameras. The main subject of this study is to increase the visibility of cameras used in railway systems. In this context, adequate illuminance of the test environment is one of the most important parameters that affect the failure detection success. With optimal lighting, the rate of fault detection increases. For this purpose, a camera, and a LED luminaire 18 W was placed on a wagon, one of the electric rail system elements. This study considered CIE140–2019 (2nd edition) standards. Thanks to the lighting made, it is easier for cameras to detect faults in the electric trains on the move. As a result, in scientific studies, especially in rail systems, the lighting of mobile test environments, such as pantograph-catenary, should be optimal. In environments where visibility conditions improve, the rate of fault detection increases.

scholarly journals On the Detection of Exploitation of Vulnerabilities Leading to the Execution of a Malicious Code

2020 ◽  
Vol 27 (2) ◽  
pp. 138-151
Author(s):  
Yury V. Kosolapov
Keyword(s):  
Symbolic Execution ◽  
Malicious Code ◽  
Abnormal Behavior ◽  
Software Protection ◽  
Intrusion Prevention ◽  
Function Calls ◽  
Normal Behavior ◽  
Subsequent Elimination ◽  
Intrusion Prevention Systems ◽  
Library Function

Software protection from exploitation of possible unknown vulnerabilities can be performed both by searching (for example, using symbolic execution) and subsequent elimination of the vulnerabilities and by using detection and / or intrusion prevention systems. In the latter case, this problem is usually solved by forming a profile of a normal behavior and deviation from normal behavior over a predetermined threshold is regarded as an anomaly or an attack. In this paper, the task is to protect a given software P from exploiting unknown vulnerabilities. For this aim a method is proposed for constructing a profile of the normal execution of the program P, in which, in addition to a set of legal chains of system and library functions, it is proposed to take into account the distances between adjacent function calls. At the same time, a profile is formed for each program. It is assumed that taking into account the distances between function calls will reveal shell code execution using system and / or library function calls. An algorithm and a system for detecting abnormal code execution are proposed. The work carried out experiments in the case when P is the FireFox browser. During the experiments the possibility of applying the developed algorithm to identify abnormal behavior when launching publicly available exploits was investigated.

scholarly journals Detection of Anomalies in the Traffic of IoT Devices

2021 ◽  
Vol 7 (4) ◽  
pp. 128-137
Author(s):  
I. Murenin
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Statistical Methods ◽  
Abnormal Behavior ◽  
Event Logs ◽  
Normal Behavior ◽  
Key Characteristics ◽  
Iot Devices ◽  
Building Technique

The article proposes an approach to finding anomalies in the traffic of IoT devices based on time series analysis and assessing normal and abnormal behavior using statistical methods. The main goal of the proposed approach is to combine statistical methods for detecting anomalies using unlabeled data and plotting key characteristics of device profiles. Within this approach the following techniques for traffic analysis has been developed and implemented: a technique for a feature extraction, a normal behavior boundary building technique and an anomaly detection technique. To evaluate the proposed approach, we used a technique for generating event logs from devices with the generation of anomalous markup. The experiments shown that the GESD-test gives the best results for anomaly detection in IoT traffic.

scholarly journals An early fault detection approach in grid-connected photovoltaic (GCPV) system

2020 ◽  
Vol 17 (2) ◽  
pp. 671
Author(s):  
N. Muhammad ◽  
H. Zainuddin ◽  
E. Jaaper ◽  
Z. Idrus
Keyword(s):  
Fault Detection ◽  
Fire Hazard ◽  
Failure Detection ◽  
Data Sets ◽  
Pv System ◽  
Acceptance Ratio ◽  
Ac Power ◽  
Detection Approach ◽  
Detection Stage ◽  
Early Fault Detection

<span>Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of grid-connected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system failure was a decision stage which performed a process based on developed logic and decision trees. The results obtained by comparing two types of GCPV system (polycrystalline and monocrystalline silicon PV system), showed that the developed algorithm could perceive the early faults upon their occurrence. Finally, when applying AR to the PV systems, the faulty PV system demonstrated 93.38 % of AR below 0.9, while the fault free PV system showed only 31.4 % of AR below 0.9.</span>

Simulation of Fault Detection in the Wireless Networked Control System

2013 ◽  
Vol 846-847 ◽  
pp. 778-781
Author(s):  
Hong Liang Guo ◽  
Shao Ying Kong
Keyword(s):  
Control System ◽  
Time Delay ◽  
Fault Detection ◽  
Wireless Network ◽  
Detection Method ◽  
State Space Model ◽  
Failure Detection ◽  
Networked Control System ◽  
Network Control ◽  
Network Control System

Wireless network control system is characterized by high uncertain delay time, thus the state of the system can not be fully observed. The fault characteristic is interfered by time delay so to be unstable, leading inaccurate fault detection. Traditional fault detection method of wireless network control system is usually based on the characteristics of the stability of the network status data. If the network has time delay fluctuations, it is unable to obtain accurate fault detection results. This paper presents a stochastic delay fault detection method. It builds a state space model of the system, analyzes the delay vector between the sensor end of the system and the controller end, depending on the delay residual signal of the system and the corresponding evaluation function to obtain the system failure detection result. The final simulation result shows that this method has high accuracy in the detection of Stability and randomness of the wireless network fault detection. Thus it is an effective wireless network control system fault detection method.

Detecting PUE Attack by Measuring Aberrational Node Behavior in CWSN

2018 ◽  
Vol 18 (01) ◽  
pp. 1850004
Author(s):  
PINAKI SANKAR CHATTERJEE ◽  
MONIDEEPA ROY
Keyword(s):  
Denial Of Service ◽  
Primary User ◽  
Sensor Nodes ◽  
Abnormal Behavior ◽  
Dos Attack ◽  
Secondary Users ◽  
Primary User Emulation ◽  
Normal Behavior ◽  
Behavior Profile ◽  
Cognitive Wireless Sensor Network

Primary User Emulation (PUE) attack is a type of Denial of Service (DoS) attack in Cognitive Wireless Sensor Network (CWSN), where malicious secondary users (SU) try to emulate primary users (PU) to maximize their own spectrum usage or obstruct other SU from accessing the spectrum. In this paper, we have designed an application to monitor the SU’s behavior with respect to the CWSN normal behavior profile towards it’s one hop neighbor. Abnormal behavior towards PUE attack of any SU helps us to identify PUE attackers in the network. Our application does not require extensive computational capabilities and memory and therefore suitable for resource constraint cognitive sensor nodes.

scholarly journals Detection and Identification of Loss of Efficiency Faults of Flight Actuators

2015 ◽  
Vol 25 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Daniel Ossmann ◽  
Andreas Varga
Keyword(s):  
Fault Detection ◽  
Surface Damage ◽  
Fault Detection And Diagnosis ◽  
Identification Algorithm ◽  
Detection Techniques ◽  
Fast Detection ◽  
Detection And Identification ◽  
Robust Fault Detection ◽  
Input Signals ◽  
Detection And Diagnosis

Abstract We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

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