Spelling suggestions: "subject:"faultdetection"" "subject:"multidetection""
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An Enhanced Approach using Time Series Segmentation for Fault Detection of Semiconductor Manufacturing ProcessTian, Runfeng 28 October 2019 (has links)
No description available.
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Implementation of a Neural Network-based In-Vehicle Engine Fault Detection SystemBremer, Mark 11 1900 (has links)
Arti cial neural networks (ANNs) are a powerful processing units inspired by the human brain. They can be used in many applications due to their pattern classi cation
abilities, ability to model complex nonlinear input-output mappings, and their ability
to adapt and learn.
The relatively new Smooth Variable Structure Filter (SVSF) has recently been
applied to the training of feedforward multilayered neural networks. It has shown to
have good accuracy and a fast speed of convergence.
In this thesis, an engine fault detection system using an ANN will be implemented.
ANNs are used in engine fault detection due to the high-noise environment that engine
operate in. Additionally the fault detection system must work while the engine is
mounted in a vehicle, which provide additional sources of noise.
The SVSF training method is evaluated and compared to other traditional training
methods. Also di erent accelerometer types are compared to evaluate whether lower
cost accelerometers can be used to keep the system cost down.
The system is tested by inducing a missing spark fault, a fault that has a complex
fault signature and is di cult to detect, especially in an engine with a high number
of cylinders. / Thesis / Master of Applied Science (MASc)
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Condition Monitoring and Fault Detection of Blade Damage in Small Wind Turbines Using Time-Series and Frequency AnalysesCostello, Luke H 01 March 2021 (has links) (PDF)
Condition monitoring systems are critical for autonomous detection of damage when operating remote wind turbines. These systems continually monitor the turbine’s operating parameters and detect damage before the turbine fails. Although common in utility-scale turbines, these systems are mostly undeveloped in distributed, small-scale turbines due to their high cost and need for specialized equipment. The Cal Poly Wind Power Research Center is developing a low-cost, modular solution known as the LifeLine system. The previous version contained monitoring equipment, but lacked decision-making capabilities.
The present work builds on the LifeLine by developing software-based detection of blade damage. Detection is done by monitoring of tower vibrations, rotor speed, and generator power output. First, testing is completed to inform algorithm design: the tower vibrational response is recorded, and blade damage is simulated by adding a mass imbalance to one blade. From these results, several algorithms are developed, and their performance is analyzed in a cross-validation study. The time-series method known as the Nonlinear State Estimation Technique and Sequential Probability Ratio Test (NSET+SPRT) is implemented first. This algorithm is highly successful, with a 93.3% rate of correct damage detection; however, it occasionally raises false alarms during normal operation. A custom-built algorithm known as the Adaptive Fast Fourier Transform (AFFT) is also built; its strength lies in its elimination of false alarms. The final system utilizes a joint monitoring approach, combining the benefits of the NSET+SPRT and AFFT. The final algorithm is successful, correctly categorizing 95.5% of data when operating above 120RPM, and raising no false alarms in normal operation. This version is then implemented for live monitoring on the Cal Poly Wind Turbine, allowing for robust and autonomous detection of blade damage.
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Fault Detection of Brahmanbaria Gas Plant using Neural NetworkSowgath, Md Tanvir, Ahmed, S. 22 December 2014 (has links)
No / In recent years, several accidents in pioneer gas
processing industries led industries to put emphasis on real-time
fault detection. Neural Network (NN) based fault (abnormal
situation) detection technique played an important role in
monitoring industrial safety. In this work, an attempt has been
made to study the fault detection of Brahmanbaria gas
processing plant using multi layered feed forward NN based
system. NN based fault detection system is trained, validated
and tested using data generated using the dynamic model.
Preliminary results show that NN based method is able to detect
the faults of Brahmanbaria Gas processing plant for fewer no of
faults.
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Industrial Extended Multi-Scale Principle Components Analysis for Fault Detection and Diagnosis of Car Alternators and StartersIsmail, Mahmoud 06 1900 (has links)
Quality assurance of electrical components of cars such as alternators and starters is
an important consideration due to both commercial and safety reasons. The focus of
this research is to develop a complete Fault Detection and Diagnosis (FDD) solution
for alternators and starters for their implementation in test cells. The FDD would
enable more reliable testing of production line parts without compromising high production
throughput. Our proposed solution includes three elements: (1) background
noise elimination; (2) fault detection and analysis; and (3) fault classi cation for fault
type identi cation.
Noise gating, Extended Multi-Scale Principle Component Analysis (EMSPCA),
and Logistic Discriminant classi er were used to perform these three elements. The
FDD strategy detects and extracts fault signatures from multiple sensors (which are
vibration and sound measurements in this research). Included in this strategy is
ltering of the background noise in sound measurements to enable operation and
maintain FDD performance in noisy conditions. The EMSPCA is the core of the
FDD strategy. EMSPCA breaks the fault into time-frequency scales using wavelets
and applies Principle Component Analysis (PCA) on each scale. This reveals the
signature of the fault. The fault signature is then examined by a classi er to match it
with the correct type of faults. The total FDD strategy is automated and no operator
intervention is required.
The advantages of the proposed FDD strategy are: (1) high accuracy in detection
and diagnosis; (2) robustness in noisy industrial conditions; and (3) no need for operators'
intervention. These advantages make the proposed FDD strategy a promising
candidate for mass industrial applications. / Thesis / Master of Applied Science (MASc)
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Fault Detection in Wastewater Treatment : Process Supervision to Improve Wastewater ReuseIvan, Heidi Lynn January 2023 (has links)
As wastewater treatment plants transition to water resource recovery facilities, the need for improved control and consequently supervision increases. Despite the large volume of research that has been performed on this topic, the use in industry is scarce. Practical implementation is challenging due to the nature of the process, and a lack of standardisation in the research results in uncertainty as to the state of the art. This is one of the main challenges identified. Experimental work is performed using the Benchmark Simulation Model No. 1 to identify monitoring requirements and evaluate the performance of univariate fault detection methods. For the former, residual based process fault signatures are used to determine minimal sensor requirements based on detectability and isolability goals. Sensor faults are the focus of the latter issue, using the Shewhart, cumulative sum, and exponentially weighted moving average control charts to detect bias and drift faults in a controlled variable sensor. The use of a standard model and known fault detection methods is useful to establish a baseline for future work. Given the lack of standardised use in industry this is considered critical. Both proposed methods emphasise ease of visualisation which is beneficial for industrial implementation.
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Distributed Fault Detection for a Class of Large-Scale Nonlinear Uncertain SystemsZhang, Qi 29 April 2011 (has links)
No description available.
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A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process DriftJin, Chao January 2015 (has links)
No description available.
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An investigation of integrated global positioning system and inertial navigation system fault detectionRamaswamy, Sridhar January 2000 (has links)
No description available.
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A baseline fault detection and exclusion algorithm for the global positioning systemBernath, Gregory N. January 1994 (has links)
No description available.
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