820 research outputs found
A review of model based and data driven methods targeting hardware systems diagnostics
System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls
Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems
Globally, the buildings sector accounts for 30% of the energy consumption and
more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and
Air Conditioning (HVAC) system is the most extensively operated component and it is
responsible alone for 40% of the final building energy usage. HVAC systems are used
to provide healthy and comfortable indoor conditions, and their main objective is to
maintain the thermal comfort of occupants with minimum energy usage.
HVAC systems include a considerable number of sensors, controlled actuators, and
other components. They are at risk of malfunctioning or failure resulting in reduced efficiency,
potential interference with the execution of supervision schemes, and equipment
deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve
their reliability, efficiency, and performance, and to provide preventive maintenance.
In this thesis work, two neural network-based methods are proposed for sensor and
actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised
sensor data validation and fault diagnosis method using an Auto-Associative Neural
Network (AANN) is developed. The method is based on the implementation of Nonlinear
Principal Component Analysis (NPCA) using a Back-Propagation Neural Network
(BPNN) and it demonstrates notable capability in sensor fault and inaccuracy
correction, measurement noise reduction, missing sensor data replacement, and in both
single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks
(CNNs) is developed for single actuator faults. It is based a data transformation in
which the 1-dimensional data are configured into a 2-dimensional representation without
the use of advanced signal processing techniques. The CNN-based actuator fault
diagnosis approach demonstrates improved performance capability compared with the
commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and
standard Neural Networks).
The presented schemes are compared with other commonly used HVAC fault diagnosis
methods for benchmarking and they are proven to be superior, effective, accurate,
and reliable. The proposed approaches can be applied to large-scale buildings with
additional zones
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications
Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit
Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines
No abstract availabl
A Predictive maintenance model for heterogeneous industrial refrigeration systems
The automatic assessment of the degradation state of industrial refrigeration systems is
becoming increasingly important and constitutes a key-role within predictive maintenance
approaches. Lately, data-driven methods especially became the focus of research in this
respect. As they only rely on historical data in the development phase, they offer great
advantages in terms of flexibility and generalisability by circumventing the need for specific
domain knowledge. While most scientific contributions employ methods emerging from
the field of machine learning (ML), only very few consider their applicability amongst
different heterogeneous systems. In fact, the majority of existing contributions in this field
solely apply supervised ML models, which assume the availability of labelled fault data for
each system respectively. However, this places restrictions on the overall applicability, as
data labelling is mostly conducted by humans and therefore constitutes a non-negligible
cost and time factor. Moreover, such methods assume that all considered fault types
occurred in the past, a condition that may not always be guaranteed to be satisfied.
Therefore, this dissertation proposes a predictive maintenance model for industrial
refrigeration systems by especially addressing its transferability onto different but related heterogeneous systems. In particular, it aims at solving a sub-problem known as
condition-based maintenance (CBM) to automatically assess the system’s state of degradation. To this end, the model does not only estimate how far a possible malfunction
has progressed, but also determines the fault type being present. As will be described
in greater detail throughout this dissertation, the proposed model also utilises techniques
from the field of ML but rather bypasses the strict assumptions accompanying supervised
ML. Accordingly, it assumes the data of the target system to be primarily unlabelled
while a few labelled samples are expected to be retrievable from the fault-free operational
state, which can be obtained at low cost. Yet, to enable the model’s intended functionality, it additionally employs data from only one fully labelled source dataset and, thus,
allows the benefits of data-driven approaches towards predictive maintenance to be further
exploited.
After the introduction, the dissertation at hand introduces the related concepts as
well as the terms and definitions and delimits this work from other fields of research.
Furthermore, the scope of application is further introduced and the latest scientific work
is presented. This is then followed by the explanation of the open research gap, from which
the research questions are derived. The third chapter deals with the main principles of the
model, including the mathematical notations and the individual concepts. It furthermore
delivers an overview about the variety of problems arising in this context and presents the
associated solutions from a theoretical point of view. Subsequently, the data acquisition
phase is described, addressing both the data collection procedure and the outcome of the
test cases. In addition, the considered fault characteristics are presented and compared
with the ones obtained from the related publicly available dataset. In essence, both
datasets form the basis for the model validation, as discussed in the following chapter. This
chapter then further comprises the results obtained from the model, which are compared
with the ones retrieved from several baseline models derived from the literature. This
work then closes with a summary and the conclusions drawn from the model results.
Lastly, an outlook of the presented dissertation is provide
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