Mr. Mohammad Shafiul Azam Profile

Mohammad Shafiul Azam

at Univ of Connecticut

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Author

Publications (5)

Proceedings Article | 25 May 2005 Paper

Fault detection, diagnosis, and data-driven modeling in HVAC chillers

Setu Namburu, Jianhui Luo, Mohammad Azam, Kihoon Choi, Krishna Pattipati

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.603742

KEYWORDS: Sensors, Principal component analysis, Systems modeling, Data modeling, Diagnostics, Databases, Process modeling, Matrices, Energy efficiency, Control systems

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Proceedings Article | 8 August 2003 Paper

Intelligent model-based diagnostics for vehicle health management

Jianhui Luo, Fang Tu, Mohammad Azam, Krishna Pattipati, Peter Willett, Liu Qiao, Masayuki Kawamoto

Proceedings Volume 5107, (2003) https://doi.org/10.1117/12.498770

KEYWORDS: Diagnostics, Model-based design, Systems modeling, Data modeling, Mathematical modeling, Sensors, Process modeling, Failure analysis, Control systems, Error analysis

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Proceedings Article | 8 August 2003 Paper

Multiphase reliability analysis of complex systems

Mohammad Azam, Fang Tu, Krishna Pattipati

Proceedings Volume 5107, (2003) https://doi.org/10.1117/12.488523

KEYWORDS: Reliability, Systems modeling, Phase modulation, Control systems, Complex systems, Failure analysis, Surface plasmons, Statistical analysis, Algorithm development, Signal to noise ratio

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Proceedings Article | 16 July 2002 Paper

Condition-based predictive maintenance of industrial power systems

Mohammad Azam, Fang Tu, Krishna Pattipati

Proceedings Volume 4733, (2002) https://doi.org/10.1117/12.475503

KEYWORDS: Failure analysis, Transformers, Capacitors, Neural networks, Multilayers, Prototyping, Carbon dioxide, Inspection, Binary data, Resistance

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Traditional static maintenance scheduling based on lifetime data and replacement upon failure is adequate for typical power users. However, in the case of high reliability/availability-oriented industries (e.g., power systems for internet data centers have a desired availability of 0.99999 and, for semiconductor fabrication plants, have availability requirement of 0.9999999), this type of preventive maintenance scheduling is inadequate. A suitable approach in these situations is the adoption of condition-based predictive maintenance. Here the system condition is evaluated by processing the information gathered from the monitors placed at different points in the system, and maintenance is performed only when the failure/malfunction prognosis dictates. In the past, for power systems, voltages, currents, power, temperature and electromagnetic quantities had been monitored along with surface inspection and material quality tests at regular intervals. Diagnostic methods are already in place to indicate problems in industrial power systems by examining these monitored quantities. However, they lack the capability of looking into distant future. With the introduction of modern digital electronics-based smart monitors, the capability of logging power quality data at micro-second intervals, advanced signal processing tools for extracting features from collected data, and data mining techniques, a new horizon in maintenance scheduling has been unveiled. Trending techniques and techniques based on neural networks, when applied to the extracted features, enable us to predict the possible failures of individual equipment and subsystems well before they manifest. This paper considers the problem of evaluating the health indices of components of a power system by making use of the monitored power-quality data and classification techniques. Health index analysis distinguishes the healthy and risky components of the system. Results of these evaluations can be fed as inputs into a system-reliability/availability analysis tool. The reliability analysis enables analysts to decide on prioritization of the maintenance options subject to budget constraints.

Proceedings Article | 20 July 2001 Paper

Capacity and reliability analyses with applications to power quality

Mohammad Azam, Fang Tu, Yuri Shlapak, Thiagalingam Kirubarajan, Krishna Pattipati, Rajaiah Karanam

Proceedings Volume 4389, (2001) https://doi.org/10.1117/12.434255

KEYWORDS: Computing systems, Reliability, Data centers, Evolutionary algorithms, Telecommunications, Power supplies, Transformers, Failure analysis, Software development, Data modeling

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