Andrew Kusiak

The University of Iowa, Industrial and Systems Engineering, Professor

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Dr. Andrew Kusiak is a Professor of Industrial and Systems Engineering and Director of Intelligent Systems Laboratory at The University of Iowa, Iowa City. He speaks frequently at international meetings, conducts professional seminars, and consults for industrial corporations. He has served on the editorial boards of over fifty journals. He is the author or coauthor of numerous books and hundreds of technical papers published in journals sponsored by professional societies, such as the Association for the Advancement of Artificial Intelligence, the American Society of Mechanical Engineers, Institute of Industrial Engineers, Institute of Electrical and Electronics Engineers, and other societies. His current research interests include applications of computational intelligence and big data in automation, energy, manufacturing, product development, sustainability, and healthcare.
Professor Kusiak is a Fellow of the Institute of Industrial Engineers and the Editor-in-Chief of the Journal of Intelligent Manufacturing. He is a holder of a Distinguished Adjunct Professorship at King Abdulaziz University, Jeddah, Saudi Arabia. His past academic appointments, include Technical University of Nova Scotia and University of Manitoba in Canada, as well as visiting faculty positions at the University of Hong Kong, University of Lion, University of Grenoble, and University of Nancy, France.
Some of his publications can be viewed at:
https://research.engineering.uiowa.edu/kusiak/
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Papers by Andrew Kusiak

Application of cyclic coherence function to bearing fault detection in a wind turbine generator under electromagnetic vibration

Mechanical Systems and Signal Processing, Mar 1, 2017

In a wind turbine generator, there is an intrinsic electromagnetic vibration originated from an a... more In a wind turbine generator, there is an intrinsic electromagnetic vibration originated from an alternating magnetic field acting on a low stiffness stator, which modulates vibration signals of the generator and impedes fault feature extraction of bearings. When defects arise in a bearing, the statistics of the vibration signal are periodic and this phenomenon is described as cyclostationarity. Correspondingly, cyclostationary analysis enables finding the degree of cyclostationarity representing potential fault modulation information. In this paper, the electromagnetic vibration acting as a disturbance source for fault feature extraction is deduced. Additionally, the spectral correlation density and cyclic coherence function used for vibration analysis are estimated. A real 2 MW wind turbine generator with a faulty bearing was tested and the vibration signals were analyzed separately using conventional demodulation analysis, cyclic coherence function, complex wavelet transform and spectral kurtosis. The analysis results have demonstrated that the cyclic coherence function can detect the fault feature of inner race successfully, while the feature is concealed by intensive electromagnetic vibration in the other three methods. The disassembled bearing of the wind turbine generator illustrates the effectiveness of the analysis result, and precautionary measures for protecting bearings in generators are suggested.

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Data-driven Fault Diagnosis of Power Transformers using Dissolved Gas Analysis (DGA)

International Journal of Technology: IJ Tech, Apr 14, 2020

A power transformer is a critical piece of equipment in a power plant for distributing electricit... more A power transformer is a critical piece of equipment in a power plant for distributing electricity, and it experiences thermal and electrical stresses during operation. Dissolved gas analysis (DGA) remains one of the most effective techniques to monitor the health of oil-filled transformers. Some traditional approaches for interpreting DGAs have been introduced. Occasionally, such approaches leave the state of the transformer uncategorized. This study proposed data-driven approaches for a fault diagnosis system based on DGA data using support vector machine (SVM). SVM is known for its robustness, good generalization capability, and unique global optimum solutions, particularly when data is limited. Backpropagation neural networks (BPNN) and extreme learning machine-radial basis function (ELM-RBF), a recent Neural Networks (NN)-based method with extremely fast computation time, were compared to SVM. An advanced technique to overcome the imbalanced data and synthetic minority oversampling technique (SMOTE) was proposed to investigate the effect on classifier performance. The model was trained and tested using IEC TC 10 databases and transformer DGA monitoring data of a thermal power plant in Jakarta. The results indicated that SVM displayed the best performance compared to ELM-RBF and BPNN. It demonstrated extremely high accuracy, while still maintaining fast computation time for all stages in the proposed multistage fault diagnosis system.

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Genetic Algorithms in Manufacturing System Design

Handbook Series for Mechanical Engineering, 2000

Hyper-automation in manufacturing industry

Journal of Intelligent Manufacturing, Dec 15, 2023

Modelling Flexible Manufacturing Systems

Infor, 1987

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(Applications of Artificial Intelligence in Engineering VI:163-178)Decomposition of Design Activities

(ASME Winter Annual Meeting:021-032)Qualitative Analysis of the Design Process

Intelligent Design Retrieving Systems Using Neural Networks

2010 2nd International Conference on Information Technology Convergence and Services, ITCS 2010: Message from the general chairs

International Conference on Information Technology, 2010

Skip to main content Iowa Research Online. ...

Knowledge-Based Programs for Manufacturing System Design

Artificial intelligence in industry, 1991

Manufacturing industry has witnessed significant developments in recent years, as measured by the... more Manufacturing industry has witnessed significant developments in recent years, as measured by the increase in the number of automated systems in use. The key to the success of these systems is effective exploitation of available resources such as machines, tools, fixtures and material handling systems. Proper use of these resources has increased productivity. At the same time, the design problems related to the modern manufacturing systems have become more complex. Designers and users of automated manufacturing systems have attempted to develop new tools to cope with these complexities. Knowledge-based systems represent a class of modern tools that have been applied to improve the design and management functions in automated manufacturing systems.

(Computer Applications in Production and Engineering:Integration Aspects:297-304)Concurrent Engineering:Simplification of the Design Process

Data mining based decision-making approach for predicting survival of kidney dialysis patients

IFAC Proceedings Volumes, Aug 1, 2003

Dialysis care is particularly complex and multiple factors may influence patient survival. The co... more Dialysis care is particularly complex and multiple factors may influence patient survival. The cost of such treatment for end stage kidney disease is high and needs attention for reducing it. Individual patient survival may depend on an intricate interrelationship between various demographic and clinical variables, medications, medical interventions and the dialysis treatment prescription. In this research, a data mining approach is used to extract knowledge regarding the interactions between the features and the outcome. There exist a complex and contradictory relationships among data mining rules that are difficult to interpret and implement. To resolve these conflicts a decision -making algorithm is developed using sixteen different classifiers. The decisionmaking algorithm employs simple and weighted voting schemes. Thus in this paper, a hybrid data mining enhanced decision making approach is used for predictions of an individual patient surviving beyond the median survival time. The concepts introduced in this research have been applied and tested using data collected at four dialysis sites.

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<title>Evolutionary computation and data mining</title>

Proceedings of SPIE, Oct 13, 2000

Future products and processes will be impacted by biology and information technology. The develop... more Future products and processes will be impacted by biology and information technology. The developments in molecular and DNA computing may revolutionize the information technology. In this paper, the product and process design challenges are discussed. Evolutionary computation and data mining are two major tools that will cope with these challenges. The basic background of the two tools as well as examples demonstrating their applications in design and manufacturing are discussed.

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Optimisation of time window size for wind power ramps prediction

Iet Renewable Power Generation, Sep 13, 2016

A new long-term wind power prediction approach based on time windows is proposed to improve the a... more A new long-term wind power prediction approach based on time windows is proposed to improve the accuracy and efficiency of wind power ramp prediction. An optimisation model is built to select the optimal time window size which is the key point of the wind power forecasting. First, a swinging door algorithm is applied to identify historical ramp events, and historical data is divided into several sections by assumed time window size. Then, windows are classified into ramp windows and nonramp windows, and the non-ramp data of ramp windows is required to be minimal. The variables, parameters, and constraints of the model are investigated in the study, and a kind of genetic algorithm is utilised to achieve the optimal solution. The model presented in this study is validated by the study case of actual wind farms, and evaluation and discussion demonstrate the validity of the proposed approach.

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Data-driven smart manufacturing

Journal of Manufacturing Systems, Jul 1, 2018

Abstract The advances in the internet technology, internet of things, cloud computing, big data, ... more Abstract The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is discussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.

Andrew Kusiak

Authors group, Jun 15, 2020

(NSF Design and Manufacturing Systems Conference:1107-1111)Algorithms for Simplification of the Design Process

Soft Computing for Optimal Planning and Sequencing of Paralleg Machining Operations

Clustering analysis : models and algorithms / 1192

In this paper, the problem of clustering observations into homogeneous groups based on given char... more In this paper, the problem of clustering observations into homogeneous groups based on given characteristics of the observations is analyzed. Three distinct integer programming formulations covering important variations of the clustering problem are developed. These variations include finding natural clusters, constraining the number of clusters and restricting the size of clusters. Efficient heuristic techniques employing Lagrangian and eigenvector based methods are developed to solve these problems.

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(IEEE Transactions on Components, Packaging, and Manufacturing Technology-Part A, 18(3):664-673)A Structured Approach for Analysis of Design Processes

Andrew Kusiak

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