2,822 research outputs found
COMBINED DEEP AND SHALLOW KNOWLEDGE IN A UNIFIED MODEL FOR DIAGNOSIS BY ABDUCTION
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.shallow knowledge, diagnosis, flow systems
Combined Deep and Shallow Knowledge in a Unified Model for Diagnosis by Abduction
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant.Faulty Diagnosis, abduction, plausibility criteria, relevant criterion
The Diagnosis by Abduction using Human Expert Knowledge
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant
Combined deep and shallow knowledge in a unified model for diagnosis by abduction
Fault Diagnosis in real systems usually involves human expert’s shallow knowledge (as pattern causes-effects) but also deep knowledge (as structural / functional modularization and models on behavior). The paper proposes a unified approach on diagnosis by abduction based on plausibility and relevance criteria multiple applied, in a connectionist implementation. Then, it focuses elicitation of deep knowledge on target conductive flow systems – most encountered in industry and not only, in the aim of fault diagnosis. Finally, the paper gives hints on design and building of diagnosis system by abduction, embedding deep and shallow knowledge (according to case) and performing hierarchical fault isolation, along with a case study on a hydraulic installation in a rolling mill plant
Increasing the robustness of autonomous systems to hardware degradation using machine learning
Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets
Computational intelligence-based prognosis for hybrid mechatronic system using improved Wiener process
In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations
Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply
The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.This research was funded by the Tunisian Ministry of Higher Education and Scientific Research
Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information
Production plants used in modern process industry must produce products that meet stringent
environmental, quality and profitability constraints. In such integrated plants, non-linearity and
strong process dynamic interactions among process units complicate root-cause diagnosis of
plant-wide disturbances because disturbances may propagate to units at some distance away
from the primary source of the upset. Similarly, implemented advanced process control
strategies, backup and recovery systems, use of recycle streams and heat integration may
hamper detection and diagnostic efforts.
It is important to track down the root-cause of a plant-wide disturbance because once
corrective action is taken at the source, secondary propagated effects can be quickly eliminated
with minimum effort and reduced down time with the resultant positive impact on process
efficiency, productivity and profitability.
In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to
incorporate and utilize knowledge about the overall process topology or interrelated physical
structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs).
Traditionally, process control engineers have intuitively referred to the physical structure of
the plant by visual inspection and manual tracing of fault propagation paths within the process
structures, such as the process drawings on printed P&IDs, in order to make logical
conclusions based on the results from data-driven analysis. This manual approach, however, is
prone to various sources of errors and can quickly become complicated in real processes.
The aim of this thesis, therefore, is to establish innovative techniques for the electronic
capture and manipulation of process schematic information from large plants such as
refineries in order to provide an automated means of diagnosing plant-wide performance
problems. This report also describes the design and implementation of a computer application
program that integrates: (i) process connectivity and directionality information from intelligent
P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii)
process know-how to aid process control engineers and plant operators gain process insight.
This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer
Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor
independent text-based XML description of the plant. The XML output was processed by a
software tool developed in Microsoft® .NET environment in this research project to
computationally generate connectivity matrix that shows plant items and their connections.
The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis
for other application and has served as precursor to other research work. The final version of
the developed software tool links statistical results of cause-and-effect analysis of process data
with the connectivity matrix to simplify and gain insights into the cause and effect analysis
using the connectivity information. Process knowhow and understanding is incorporated to
generate logical conclusions.
The thesis presents a case study in an atmospheric crude heating unit as an illustrative example
to drive home key concepts and also describes an industrial case study involving refinery
operations. In the industrial case study, in addition to confirming the root-cause candidate, the
developed software tool was set the task to determine the physical sequence of fault
propagation path within the plant.
This was then compared with the hypothesis about disturbance propagation sequence
generated by pure data-driven method. The results show a high degree of overlap which helps
to validate statistical data-driven technique and easily identify any spurious results from the
data-driven multivariable analysis. This significantly increase control engineers confidence in
data-driven method being used for root-cause diagnosis.
The thesis concludes with a discussion of the approach and presents ideas for further
development of the methods
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