Basel Shadid, PhD, PEng, PMP
To pursue challenging research and teaching assignments in the areas of advanced manufacturing systems, machine-learning and temporal data mining, and their applications in decision making processes, focusing on manufacturing systems decision support tools.
Areas of Interest
Production Systems Management, Industrial Automation, Machine Learning, Temporal Data Mining, Decision Making and Manufacturing Execution Systems
My research in decision support tools in manufacturing systems is motivated by the years I spent in GMPT plants working on production lines management. I came to realize that conventional data analysis tools are not enough for today’s decision makers and there is a real need to utilize advanced machine learning and data mining algorithms.
In my PhD thesis I used frequent episodes analysis to identify episodes of performance deterioration in a component of the manufacturing system and to model the deterioration in a sequence of states using HMM. Modeling the deterioration in states allowed the utilization of Bayes decision theory to identify optimal decisions for the component replacement by minimizing the expected risk over the component life cycle.
This work encouraged me to pursue more challenging research in machine learning and temporal data mining in other manufacturing areas, especially algorithms that are designed to solve problems with large number of variables. In my PhD research I found that the uncertainty in the data and prior information along with the large number of variables to optimize multiple manufacturing metrics simultaneously are the hardest challenges. I am looking forward to joining a research group that tackles these challenges specially in manufacturing systems.
2008 McMaster University Hamilton, Ontario PhD, ME
Research Topic: Manufacturing Execution Systems
Thesis Title: Decision Making in Manufacturing Systems: an Integrated Throughput, Quality and Maintenance Model Using HMM
1998 Cleveland State University Cleveland, Ohio MS, I&ME
Research Area: Ultrasound in NDE for Quality Control Applications
1996 University of Jordan Amman , Jordan BS, ME
Major in Robotics Engineering
- 2007 GM “Boss” Kettering award; GM highest technical award
- 2006 GM R&D Most Valuable Colleague award
2009 – Present King Saud University Riyadh, SA
Assistant Professer - Industrial Engineering Department
2005 – 2009 General Motors St. Catharines, ON
Sr. Manufacturing Engineer – Machine Controls
Managing controls systems projects in engine machining and assembly lines including:
- Setting machines controls specifications, negotiation with machines builders, machines run-off and installation.
- Leading continuous improvement projects in productivity, quality control and cost reduction.
- Developing web-based decision support tools to improve system uptime, product quality and maintenance optimization. Part of GM R&D team to develop GMPT new generation of Plant Monitoring and Control System. (Maintenance Toolbox)
2002 – 2005 General Motors St. Catharines, ON
Manufacturing Engineer I – Machine Controls
- Managing controls preventive and predictive maintenance activities. Leading emergency breakdown and reactive maintenance teams
- Design and deployment of automated quality control systems for product monitoring including laser measurements and vision system inspections.
1999 – 2002 General Motors St. Catharines, ON
Manufacturing Engineer – Machine Controls
- Focus factory and continuous improvement activities for the engine assembly including maintenance support, quality control and other engineering projects.
- Support the training department in setting technical courses for the skilled trade group in machines controls and reactive maintenance fields
1998–1999 Zagar, Inc. Cleveland, OH
- Design controls systems for custom CNC machines to the automotive industry including motion controller, PLC and HMI programming.
- Research and developments for the NC drill head feed units including product testing, design improvement and process failure modes and effects analysis
- Customer support for system troubleshooting and field service trips.
1997- 1998 Cleveland State University Cleveland , OH
Lab Instructor and Research Assistant
- Teaching conventional and CNC machining processes and manufacturing systems lab.
- Setting-up and programming DAQ system for NDE research on turning processes and material strength analysis
- McMaster / St. Catharines Engine: Identifying optimal decisions for the component replacement by minimizing the expected risk over the component life cycle. The research provided a tool to estimate performance deterioration states and is detailed in my PhD thesis.
- GMR / St. Catharines Engine: Maintenance Toolbox, a web based plant support system for maintenance, quality and throughput decisions. Based on this project, “Production System Performance Prediction Using Machines Faults” patent was filed
- GMR / Queens University: FTQ Supervisory controller to provide high level immediate corrective actions. It integrated a cognitive model of quality problem resolution with a context sensitive human-machine interface. The work accomplished in this research was filed in patent under “First-Time-Quality Supervisory Controller”
- GMR / St. Catharines Engine: Identifying episodes of correlated machine faults among multiple machines to determine the sequential relation of failures and eventually the failure root cause. The work in this project was filed in patent “Root-cause diagnostics of machine and system faults using Temporal Data Mining”
- GMR / St. Catharines Engine: Predicting individually optimized ranks for Key Performance Indicators (KPI) for plant floor systems events as well as the expected error of such predictions. The work in this project was filed under TMI “Prognostics Performance Evaluation & Error Correction Control”
- GMR / St. Catharines Engine: Providing real-time indications of future changes on plant floor systems states by using modified particle filter. The work in the research was filed under TMI “Non-Parametric Data-Driven Event State Transition Prediction”
- St. Catharines Engine: Analysis of assembly line constraint progression based on the rate of change of block/starve functions and throughout losses. This research provided a tool to rank simultaneous constraints on real time.
- GMR / MIT: Quality / Quantity modeling and analysis of production lines subject to uncertainty. The research investigated the effects of quality and operational failures on repair decisions.
- GMR / McMaster University: Monitoring deterioration in engine test stations by correlating process variables and variations in test results. The research provided a preventive maintenance tool to identify deteriorating components in test stations.
- B. Shadid; Pulak Bandyopadhyay; G. Xiao; J. Arinez; R. Dwibhashyam; L. Barajas; “Production System Performance Prediction Using Machines Faults”
- J. Arinez; B. Shadid; “First-Time-Quality Supervisory Controller”
- K. P. Unnikrishnan; B. Shadid; P. S. Sastry; Srivatsan Laxman; “Root-cause Diagnostics of Machine and System Faults Using Temporal Data Mining”
- S. Biller; TMI “Prognostics Performance Evaluation & Error Correction Control”
- L. Barajas; B. Shadid; TMI “Non-Parametric Data-Driven Event State Transition Prediction”
- B. Shadid; G. Xiao; Q. Chang; TMI “Data Driven Bottleneck Detection of Manufacturing Systems”
- R. Menassa; G. Xiao; B. Shadid; R. Dwibhashyam; W. Cai; N. Grzymkowski; S. Clarkston; Defensive Publication “Integrated Real Time Quality Index (Q-Ind)
- McMaster University: June 2003 “e – Manufacturing applications in plant floor maintenance management”. The presentation was given to MMRI for the St. Catharines engine deployment of e-manufacturing tools in maintenance management.
- MIT July 2005 “Decision making processes in the plant floor based on components states and performance metrics”. The presentation was given to LMP research group.
- Industrial Engineering Chapter - Saudi Council of Engineers in conjunction with Saudi Chapter (No. 246) American Institute of Industrial Engineers; March 2010 “An Innovative Model for Performance Optimization in the Automotive Industry”.
- S. Laxman, B. Shadid, K. P. Unnikrishnan and P. S. Sastry, “Temporal data mining for root-cause analysis of machine faults inautomotive assembly lines” (submitted to IEEE Transactions on Automation Science and Engineering)
- B. Shadid “Identifying optimal replacement state for manufacturing system components suing frequent episodes and HMM for throughput, quality and maintenance data” (in preparation)
- Automation: GE Proficy, RSlogix, Logic 5, Panel Builder, LabVIEW, VB.Net, FloPro, GE VersaPro, LogicMaster, Taylor ProWorks, Vision system and different motion controller products.
- CNC: Power Mate D, GE16 controller, GE Fuji AF-300 Drives
- Programming: VB.Net , Java , C/C++, and Fortran
- CAD/CAM: AutoCAD, Mechanical Desktop, CADkey and MasterCAM.
- Statistics and Data Mining: MatLab, R, Weka and Yale.
- Database: MS SQL and Oracle
- Network: Ethernet TCP/IP, Modbus+, GE Genius, Profibus, MAP, and DeviceNet.