4,205 research outputs found
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology
https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd
Innovative Techniques for Testing and Diagnosing SoCs
We rely upon the continued functioning of many electronic devices for our everyday welfare,
usually embedding integrated circuits that are becoming even cheaper and smaller
with improved features. Nowadays, microelectronics can integrate a working computer
with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC).
SoCs are also employed on automotive safety-critical applications, but need to be tested
thoroughly to comply with reliability standards, in particular the ISO26262 functional
safety for road vehicles.
The goal of this PhD. thesis is to improve SoC reliability by proposing innovative
techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals,
and GPUs. The proposed approaches in the sequence appearing in this thesis are described
as follows:
1. Embedded Memory Diagnosis: Memories are dense and complex circuits which
are susceptible to design and manufacturing errors. Hence, it is important to understand
the fault occurrence in the memory array. In practice, the logical and physical
array representation differs due to an optimized design which adds enhancements to
the device, namely scrambling. This part proposes an accurate memory diagnosis
by showing the efforts of a software tool able to analyze test results, unscramble
the memory array, map failing syndromes to cell locations, elaborate cumulative
analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing
syndromes were analyzed as case studies gathered on an industrial automotive
32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually,
and results were confirmed by real photos taken from a microscope.
2. Functional Test Pattern Generation: The key for a successful test is the pattern applied
to the device. They can be structural or functional; the former usually benefits
from embedded test modules targeting manufacturing errors and is only effective
before shipping the component to the client. The latter, on the other hand, can be
applied during mission minimally impacting on performance but is penalized due
to high generation time. However, functional test patterns may benefit for having
different goals in functional mission mode. Part III of this PhD thesis proposes
three different functional test pattern generation methods for CPU cores embedded
in SoCs, targeting different test purposes, described as follows:
a. Functional Stress Patterns: Are suitable for optimizing functional stress during
I
Operational-life Tests and Burn-in Screening for an optimal device reliability
characterization
b. Functional Power Hungry Patterns: Are suitable for determining functional
peak power for strictly limiting the power of structural patterns during manufacturing
tests, thus reducing premature device over-kill while delivering high test
coverage
c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns
with functional ones, allowing its execution periodically during mission.
In addition, an external hardware communicating with a devised SBST was proposed.
It helps increasing in 3% the fault coverage by testing critical Hardly
Functionally Testable Faults not covered by conventional SBST patterns.
An automatic functional test pattern generation exploiting an evolutionary algorithm
maximizing metrics related to stress, power, and fault coverage was employed
in the above-mentioned approaches to quickly generate the desired patterns. The
approaches were evaluated on two industrial cases developed by STMicroelectronics;
8051-based and a 32-bit Power Architecture SoCs. Results show that generation
time was reduced upto 75% in comparison to older methodologies while
increasing significantly the desired metrics.
3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices
are suitable for generating structural patterns, testing and activating mitigation techniques,
and validating robust hardware and software applications. GPGPUs are
known for fast parallel computation used in high performance computing and advanced
driver assistance where reliability is the key point. Moreover, GPGPU manufacturers
do not provide design description code due to content secrecy. Therefore,
commercial fault injectors using the GPGPU model is unfeasible, making radiation
tests the only resource available, but are costly. In the last part of this thesis, we
propose a software implemented fault injector able to inject bit-flip in memory elements
of a real GPGPU. It exploits a software debugger tool and combines the
C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in
program variables. The goal is to validate robust parallel algorithms by studying
fault propagation or activating redundancy mechanisms they possibly embed. The
effectiveness of the tool was evaluated on two robust applications: redundant parallel
matrix multiplication and floating point Fast Fourier Transform
Federated Robust Embedded Systems: Concepts and Challenges
The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements.
While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development.
During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs
A framework and methods for on-board network level fault diagnostics in automobiles
A significant number of electronic control units (ECUs) are nowadays networked
in automotive vehicles to help achieve advanced vehicle control and eliminate
bulky electrical wiring. This, however, inevitably leads to increased complexity in
vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at
service centres, by using only diagnostic trouble codes logged by conventional onboard
diagnostics, can become unwieldy especially when dealing with intermittent
faults in complex networked electronic systems. This can result in inaccurate and
time consuming diagnostics due to lack of real-time fault information of the
interaction among ECUs in the network-wide perspective.
This thesis proposes a new framework for on-board knowledge-based
diagnostics focusing on network level faults, and presents an implementation of a
real-time in-vehicle network diagnostic system, using case-based reasoning. A
newly developed fault detection technique and the results from several practical
experiments with the diagnostic system using a network simulation tool, a
hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real
ECUs networked on a test rig are also presented. The results show that the new
vehicle diagnostics scheme, based on the proposed new framework, can provide
more real-time network level diagnostic data, and more detailed and self-explanatory
diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in
terms of detecting message communication faults. In particular, the underlying
incipient network problems that are ignored by the conventional on-board
diagnostics are picked up for thorough fault diagnostics and prognostics which can
be carried out by a whole-vehicle fault management system, contributing to the
further development of intelligent and fault-tolerant vehicles
The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings
Modern condition monitoring systems (CMS) collect and process enormous amount of data in order to provide the earliest and most dependable information of fault development within any of the machine components and their operation combined. According to numerous studies one of the most fault susceptible mechanical elements in rotating machinery are rolling element bearings. Although reliable techniques for their diagnostics are already proposed, the new investigation is needed. According to authors experience in many industrial applications the operators are obligated to simultaneously track hundreds of diagnostic estimates, such as signals energy, its peakedness or narrowband characteristics for localized faults. As mentioned, for a vibration-based CMS of single wind turbine there are nearly 150 of them. Therefore, the authors employ a fuzzy logic approach for integration of bearing diagnostic features. A new estimate that carry most relevant information about bearing condition is discussed. The reasoning is presented on simulated data that mimics real rotating machine
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Detecting anomalies in multivariate time series from automotive systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the automotive industry test drives are conducted during the development of new
vehicle models or as a part of quality assurance for series vehicles. During the test drives, data is recorded for the use of fault analysis resulting in millions of data points. Since multiple vehicles are tested in parallel, the amount of data that is to be analysed is tremendous. Hence, manually analysing each recording is not feasible. Furthermore the complexity of vehicles is ever-increasing leading to an increase of the data volume and complexity of the recordings. Only by effective means of analysing the recordings, one can make sure that the effort put in the conducting of test drives pays off. Consequently, effective means of test drive analysis can become a competitive advantage.
This Thesis researches ways to detect unknown or unmodelled faults in recordings
from test drives with the following two aims: (1) in a data base of recordings, the
expert shall be pointed to potential errors by reporting anomalies, and (2) the time
required for the manual analysis of one recording shall be shortened. The idea to achieve the first aim is to learn the normal behaviour from a training set of recordings and then to autonomously detect anomalies. The one-class classifier āsupport vector data descriptionā (SVDD) is identified to be most suitable, though it suffers from the need to specify parameters beforehand. One main contribution of this Thesis is a new autonomous parameter tuning approach, making SVDD applicable to the problem at hand. Another vital contribution is a novel approach enhancing SVDD to work with multivariate time series. The outcome is the classifier āSVDDsubseqā that is directly applicable to test drive data, without the need for expert knowledge to configure or tune the classifier. The second aim is achieved by adapting visual data mining techniques to make the manual analysis of test drives more efficient. The methods of āparallel coordinatesā and āscatter plot matricesā are enhanced by sophisticated filter and query operations, combined with a query tool that allows to graphically formulate search patterns. As a combination of the autonomous classifier āSVDDsubseqā and user-driven visual data mining techniques, a novel, data-driven, semi-autonomous approach to detect unmodelled faults in recordings from test drives is proposed and successfully validated
on recordings from test drives. The methodologies in this Thesis can be used as a
guideline when setting up an anomaly detection system for own vehicle data
Sensors Fault Diagnosis Trends and Applications
Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis
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