2,434 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
A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline
This study proposes a framework for the automated hyperparameter optimization
of a bearing fault detection pipeline for permanent magnet synchronous motors
(PMSMs) without the need of external sensors. A automated machine learning
(AutoML) pipeline search is performed by means of a genetic optimization to
reduce human induced bias due to inappropriate parameterizations. For this
purpose, a search space is defined, which includes general methods of signal
processing and manipulation as well as methods tailored to the respective task
and domain. The proposed framework is evaluated on the bearing fault detection
use case under real world conditions. Considerations on the generalization of
the deployed fault detection pipelines are also taken into account. Likewise,
attention was paid to experimental studies for evaluations of the robustness of
the fault detection pipeline to variations of the motors working condition
parameters between the training and test domain. The present work contributes
to the research of fault detection on rotating machinery in the following
terms: (1) Reduction of the human induced bias to the data science process,
while still considering expert and task related knowledge, ending in a generic
search approach (2) tackling the bearing fault detection task without the need
for external sensors (sensorless) (3) learning a domain robust fault detection
pipeline applicable to varying motor operating parameters without the need of
re-parameterizations or fine-tuning (4) investigations on working condition
discrepancies with an excessive degree to determine the pipeline limitations
regarding the abstraction of the motor parameters and the pipeline
hyperparametersComment: 8 pages, 4 figures, 5 tables, ieee conference paper template use
Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection
The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model
Faults Identification in Three-Phase Induction Motors Using Support Vector Machines
Induction motor is one of the most important motors used in industrial applications. The operating conditions may sometime lead the machine into different fault situations. The main types of external faults experienced by these motors are over loading, single phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage and over voltage. The machine should be shut down when a fault is experienced to avoid damage and for the safety of the workers. Computer based relays monitor the machine and disconnect it during the faults. The relay logic used to identify these faults requires sophisticated signal processing techniques for fast and reliable operation. Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN) have been applied in induction motor relays. Though the ANN based methods are reliable, the selection of the ANN structures and training is time consuming. Recently it is observed that the AI techniques such as Support Vector Machines (SVM) are alleviating some of the limitations of ANN method.
The objectives of this study are to develop a SVM based induction motor external faults identifier and study its performance with real-time induction motor faults data. Data collected from a 1/3 hp, 208 V three-phase squirrel cage induction motor is used in this project. Radial Bases Function Kernel is used to train and test the SVM, though the effect of other Kernel functions was also studied. The proposed SVM method uses RMS values of three-phase voltages and currents as inputs. The testing results showed the efficacy of the SVM based method for identifying the external faults experienced by 3-phase induction motors. It is observed that the performance of the SVM based method is better than the ANN based method both in model creation and testing accuracy
Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation
International audienceCondition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes
Advances in power quality analysis techniques for electrical machines and drives: a review
The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems
Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake.
The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs.
This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof.
Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation
On the identifiability, parameter identification and fault diagnosis of induction machines
PhD ThesisDue to their reliability and low cost, induction machines have been widely utilized in a large
variety of industrial applications. Although these machines are rugged and reliable, they are
subjected to various stresses that might result in some unavoidable parameter changes and
modes of failures. A common practice in induction machine parameter identification and fault
diagnosis techniques is to employ a machine model and use the external measurements of
voltage, current, speed, and/or torque in model solution. With this approach, it might be possible
to get an infinite number of mathematical solutions representing the machine parameters,
depending on the employed machine model. It is therefore crucial to investigate such possibility
of obtaining incorrect parameter sets, i.e. to test the identifiability of the model before being
used for parameter identification and fault diagnosis purposes. This project focuses on the
identifiability of induction machine models and their use in parameter identification and fault
diagnosis.
Two commonly used steady-states induction machine models namely T-model and inverse Γ-
model have been considered in this thesis. The classical transfer function and bond graph
identifiability analysis approaches, which have been previously employed for the T-model, are
applied in this thesis to investigate the identifiability of the inverse Γ-model. A novel algorithm,
the Alternating Conditional Expectation, is employed here for the first time to study the
identifiability of both the T- and inverse Γ-models of the induction machine. The results
obtained from the proposed algorithm show that the parameters of the commonly utilised Tmodel
are non-identifiable while those of the inverse Γ-model are uniquely identifiable when
using external measurements. The identifiability analysis results are experimentally verified by
the particle swarm optimization and Levenberg-Marquardt model-based parameter
identification approaches developed in this thesis.
To overcome the non-identifiability problem of the T-model, a new technique for induction
machine parameter estimation from external measurements based on a combination of the
induction machine’s T- and inverse Γ-models is proposed. Results for both supply-fed and
inverter-fed operations show the success of the technique in identifying the parameters of the
machine using only readily available measurements of steady-state machine current, voltage
and speed, without the need for extra hardware.
ii
A diagnosis scheme to detect stator winding faults in induction machines is also proposed in
this thesis. The scheme uses time domain features derived from 3-phase stator currents in
conjunction with particle swarm optimization algorithm to check characteristic parameters of
the machine and detect the fault accordingly. The validity and effectiveness of the proposed
technique has been evaluated for different common faults including interturn short-circuit,
stator winding asymmetry (increased resistance in one or more stator phases) and combined
faults, i.e. a mixture of stator winding asymmetry and interturn short-circuit. Results show the
accuracy of the proposed technique and it is ability to detect the presence of the fault and
provide information about its type and location.
Extensive simulations using Matlab/SIMULINK and experimental tests have been carried out
to verify the identifiability analysis and show the effectiveness of the proposed parameter
identification and fault diagnoses schemes. The constructed test rig includes a 1.1 kW threephase
test induction machine coupled to a dynamometer loading unit and driven by a variable
frequency inverter that allows operation at different speeds. All the experiment analyses
provided in the thesis are based on terminal voltages, stator currents and rotor speed that are
usually measured and used in machine control.Libya, through the Engineering Faculty of Misurata-
Misurata Universit
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