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Skip to Search Results- 12Fault Detection
- 3Machine Learning
- 2Fault Diagnosis
- 2Process Monitoring
- 1Adaptive Sampling
- 1Alarm Monitoring
- 1Bahador Rashidi
- 1Gonzalez, Ruben T
- 1Hajizadeh, Mohammad
- 1Nadadoor Srinivasan, Venkat R.
- 1Rezvan Rafiee Alavi
- 1Sammaknejad, Nima
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Fall 2014
While there has been much literature in the area of system monitoring and diagnosis, most of these techniques have a relatively small scope in terms of the faults and performance issues that they are built to detect. When implementing several monitors simultaneously on a single process, a single...
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Data-Driven Fault Detection, Isolation and Identification of Rotating Machinery: with Applications to Pumps and Gearboxes
DownloadFall 2012
Fault diagnosis plays an important role in the reliable operation of rotating machinery. Data-driven approaches for fault diagnosis rely purely on historical data. Depending on how a diagnosis decision is made, this thesis divides data-driven fault diagnosis approaches into two groups:...
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Spring 2020
As the process control industry and production lines become highly complex and significantly invested with high-dimensional variables, process health monitoring attracts more attention from the domain experts and process operators. Since data is ubiquitous nowadays thanks to the advanced computer...
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Fall 2016
Nowadays, industrial processes are becoming highly complex and integrated due to the applications of advanced distributed control systems. As multiple production units with thousands of actuators are operating at the same time, the reliability issue of process plants naturally arises. To ensure...
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Fault Detection and Diagnosis in Nonlinear Systems, with a Focus on Mining Truck Suspension Strut
DownloadSpring 2014
Classical fault detection methods do not completely satisfy the reliability requirement for complex and highly nonlinear stochastic systems. One solution to this problem is to use more advances fault detection methods such as multiple models to simulate system in different operating...
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Fall 2015
A large volume of literature exists on fault detection and isolation for industrial processes. In a general view, these various methods may be divided into process model based and process history based fault diagnosis. In both classes, there has been a recent focus on extracting the temporal...
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Fall 2016
This thesis investigates the integrated fault detection, estimation and fault tolerant control problem for linear systems and Lipschitz nonlinear systems. Faults and disturbances are taken into consideration in a unified formulation. A H-infinity observer-based fault detection filter (FDF) is...
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Spring 2021
This thesis investigates the use of general value functions for detecting anomalous behavior in machines. Identifying abnormal behavior is critical for ensuring the safety and reliability of any machine or industrial process. When the cause of these anomalies is due to accumulated wear on...
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Monitoring of Industrial Processes via Non-stationary Probabilistic Slow Feature Analysis Machine Learning Algorithm
DownloadSpring 2020
This research develops a first of its kind machine learning (ML) algorithm, called probabilistic slow feature analysis (PSFA), that monitors and detects faults for non-stationary industrial processes. The novelty of this ML algorithm is that it can monitor and detect faults for non-stationary...
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Near-Field to Far-Field Transformation and Fault Detection Using Adaptive Sampling and Machine Learning in Source Reconstruction Method
DownloadFall 2019
Until not so long ago, near-field and far-field measurement techniques were the two prominent approaches to evaluating antennas. A direct far-field measurement can be conducted in outdoor or indoor environments. The measurement of small antennas can be performed in anechoic chambers. For large...