Data Analysis Techniques for Engineers & Technologists

Data Analysis Techniques for Engineers & Technologists

:Program Objectives

1. To provide delegates with a working vocabulary of analytical terms to enable them to converse with people who are experts in the areas of data analysis, statistics and probability, and to be able to read and comprehend common textbooks and journal articles in this field.
2. To provide delegates with both an understanding and practical experience of a range of the more common analytical techniques and data representation methods, which have direct relevance to a wide range of analytical problems. To give delegates the ability to recognize which types of analysis are best suited to particular types of problems.
3. To give delegates sufficient background and theoretical knowledge to be able to judge when an applied technique will likely lead to incorrect conclusions. To provide delegates with an overview of the main data analysis applications within engineering systems

:Course Outline

The Basics Sources of data, data sampling, data accuracy, data completeness, simple representations, dealing with practical issues. Workshop using centrifugal pump performance data. Fundamental Statistics Mean, average, median, mode, rank, variance, covariance, standard deviation, "lies, more lies and statistics", compensations for small sample sizes, descriptive statistics. Workshop using production data from a batch fermentation process. Data Mining and Representation Single, two and multi-dimensional data visualization, trend analysis, how to decide what it is that you want to see, box and whisker charts, common pitfalls and problems. Workshop using petrochemical plant control data. Probability and Confidence Probability theory, properties of distributions, expected values, setting confidence limits, risk and uncertainty, normal distribution, weibull distribution, binomial distribution, exponential distribution. Workshop using statistical process control data in the machinery protection system of a turbine-compressor installation. Histograms & Frequency of Occurrence Histograms, Pareto analysis (sorted histogram), cumulative percentage analysis, the law of diminishing return, percentile analysis. Workshop using historical failure data from a group of reciprocating compressors. Frequency Analysis The Fourier transform, periodic and a-periodic data, inverse transformation, practical implications of sample rate, dynamic range and amplitude resolution. Workshop using vibration data from a large gearbox. Regression Analysis and Curve Fitting Linear and non-linear regression, order; best fit; minimum variance, maximum likelihood, least squares fits, curve fitting theory, linear, exponential and polynomial curve fits, predictive methods. Workshop using failure data from large three phase induction motors, and remaining lifetime prediction. Data Comparison Correlation analysis, the autocorrelation function, Mahalanobis’ distance, practical considerations of data set dimensionality. Workshop using diesel engine performance and pollutant emission data. The Power of Excel and MATLAB. Pivot tables, the analytical toolbox, internet-based analysis tools and macros, dynamic spreadsheets, sensitivity analysis, visualization. Workshop involving step-by-step examples of the advanced capabilities of spreadsheets and the exploitation of ready-written resources. Quality Control Applications Terminology, control charts, statistical control, estimating the process mean and variation, capability indexes, control charts for attribute data. Workshop on constructing the x bar and R charts for a milling process. Reliability Evaluation Applications Terminology, reliability definition and concepts, reliability functions, a reliability evaluation process. Workshop on evaluating the hazard rate, survivor function, failure density function, and cumulative failure distribution function for typical industrial equipment.

?Who should attend

The course has been designed for engineers, technologists, and managers whose jobs involve the manipulation, representation, and analysis of data. Basic familiarity with PC's and in particular with Microsoft. Excel. is assumed. Aside from this, no other software familiarity or previous data analysis experience is required