Process Data Analysis Practice Series v1.0

Series Overview

Learn practical skills to effectively analyze vast amounts of time series data obtained from chemical processes, and utilize them for process optimization, anomaly detection, and quality prediction. This series develops practical skills through over 50 useful Python code examples, from statistical methods to machine learning.

Intermediate to Advanced 📖 Reading time: 150-180 minutes 💻 Code examples: 50

🎯 What You Will Learn in This Series

Implementation of preprocessing and advanced analysis methods for time series data
Statistical analysis of multivariate process data and building machine learning models
Design and implementation of real-time anomaly detection systems
Improving prediction model accuracy through feature engineering
Practical data science skills immediately applicable in industrial settings

Learning Roadmap

This series consists of five chapters, progressing from fundamentals to applications step by step.

graph LR A[Chapter 1
Time Series Analysis Basics] --> B[Chapter 2
Multivariate Analysis] B --> C[Chapter 3
Anomaly Detection] C --> D[Chapter 4
Feature Engineering] D --> E[Chapter 5
Real-time Analysis] style A fill:#e3f2fd,stroke:#11998e,stroke-width:2px style B fill:#e8f5e9,stroke:#11998e,stroke-width:2px style C fill:#fff3e0,stroke:#11998e,stroke-width:2px style D fill:#f3e5f5,stroke:#11998e,stroke-width:2px style E fill:#ffe0e0,stroke:#11998e,stroke-width:2px

Chapter Structure

Chapter 1: Fundamentals of Time Series Data Analysis

Understand the time series characteristics of process data and learn fundamental techniques from preprocessing to statistical testing and prediction model building. Provides 10 practical code examples including ARIMA models, exponential smoothing, and change point detection.

📖 Reading time: 30-35 minutes | 💻 Code examples: 10 | 🎓 Difficulty: Intermediate

Time series data preprocessing (missing value imputation, outlier detection)
Stationarity testing and trend decomposition
Autocorrelation analysis and ARIMA modeling
Change point detection and pattern matching

Chapter 2: Multivariate Process Data Analysis

Analyze correlations between multiple process variables and implement multivariate statistical methods such as Principal Component Analysis (PCA) and Partial Least Squares (PLS). Learn 10 code examples applicable to process monitoring and soft sensor construction.

📖 Reading time: 30-35 minutes | 💻 Code examples: 10 | 🎓 Difficulty: Intermediate to Advanced

Process monitoring using Principal Component Analysis (PCA)
Soft sensor construction using Partial Least Squares (PLS)
Canonical Correlation Analysis (CCA) and variable selection
Dynamic PCA (DPCA) and multivariate time series analysis

Chapter 3: Anomaly Detection and Diagnosis

Build anomaly detection systems combining statistical methods and machine learning. Develop practical skills through 10 implementation examples including Hotelling T², SPE statistics, Isolation Forest, and Autoencoder.

📖 Reading time: 30-35 minutes | 💻 Code examples: 10 | 🎓 Difficulty: Advanced

Statistical Process Control (SPC) and Hotelling T²
Anomaly detection using One-Class SVM and Isolation Forest
Nonlinear anomaly detection using Autoencoder
Anomaly diagnosis and Root Cause Analysis (RCA)

Chapter 4: Feature Engineering and Prediction Models

Learn methods to extract useful features from process data and build high-accuracy prediction models. Implement 10 advanced techniques including time window statistics, wavelet transform, and deep learning.

📖 Reading time: 30-35 minutes | 💻 Code examples: 10 | 🎓 Difficulty: Advanced

Time window statistics and derived feature generation
Frequency domain features using wavelet transform
Time series prediction using LSTM/GRU
Modeling long-term dependencies using Transformer

Chapter 5: Real-time Data Analysis Systems

Learn the design and implementation of real-time data analysis systems for actual industrial operations. Master 10 practical techniques including streaming data processing, online learning, and edge inference.

📖 Reading time: 30-35 minutes | 💻 Code examples: 10 | 🎓 Difficulty: Advanced

Streaming data processing and buffering strategies
Online learning and model updating
Real-time anomaly detection alert system
Edge computing and lightweight model deployment

Prerequisites

Field	Required Skills
PI Fundamentals	Basic operations of PI Data Archive, PI Vision, PI AF (PI Introduction Series completion level)
Python Programming	Basic experience with NumPy, pandas, scikit-learn, Matplotlib
Statistics Fundamentals	Basic concepts of descriptive statistics, hypothesis testing, regression analysis
Chemical Engineering Knowledge	Basic understanding of process variables (temperature, pressure, flow rate, etc.)
Machine Learning (Recommended)	Basic concepts of supervised and unsupervised learning (useful from Chapter 3 onwards)

Recommended Learning Environment

💻 Development Environment Setup

Required Libraries:

Python 3.8 or higher
NumPy, pandas, scikit-learn, Matplotlib, seaborn
statsmodels (time series analysis)
PyWavelets (wavelet transform)
TensorFlow/PyTorch (deep learning, Chapters 4-5)

⚠️ About Datasets

Code examples in this series use simulation data with typical chemical process parameters (reaction temperature, pressure, flow rate, concentration, etc.). To retrieve data from an actual PI System, use PI Web API or PI SDK for Python.

Learning Objectives

Upon completing this series, you will acquire the following skills:

Basic Understanding Level

Explain the characteristics of time series data (trend, seasonality, stationarity)
Understand the principles and application scenarios of multivariate statistical methods (PCA, PLS)
Compare types and features of anomaly detection algorithms
Explain the importance and methods of feature engineering

Practical Skills Level

Automate PI data preprocessing and quality checks
Predict process values using ARIMA models and LSTM
Build anomaly detection systems combining statistical methods and machine learning
Implement soft sensors to estimate difficult-to-measure variables
Process and analyze real-time data streams

Application Level

Select and apply optimal analysis methods according to process characteristics
Perform model tuning to improve anomaly detection accuracy
Design systems considering actual industrial operations
Design appropriate role distribution between edge computing and cloud

Frequently Asked Questions (FAQ)

Q1: Can I learn without completing the PI Introduction Series?

Since this series focuses on data analysis methods, learning is possible without detailed knowledge of PI System. However, having a basic understanding of data retrieval methods from PI and tag structure will make practical application smoother.

Q2: Can I understand Chapters 3 and beyond without machine learning experience?

Each chapter provides concise explanations of necessary theory, but basic experience with scikit-learn is desirable. If you want to learn machine learning fundamentals, prior study of introductory books such as "Hands-On Machine Learning with Scikit-Learn and TensorFlow" is recommended.

Q3: Will code examples work with actual process data?

All code examples are designed generically and can be applied to real process data. By replacing the data acquisition part (connection to PI) according to your actual environment, they can be used as-is.

Q4: What computational resources are required for real-time analysis?

Real-time analysis covered in Chapter 5 can be executed on a typical workstation (CPU: Intel Core i5 or higher, RAM: 8GB or higher). For large plants (1000+ tags), using GPU-equipped machines or cloud environments is recommended.

Q5: How much time does it take to study each chapter?

Reading time for each chapter is 30-35 minutes, but to actually run code examples and check behavior by changing parameters, an additional 2-3 hours is recommended. For the entire series, allow 20-25 hours of study time.

Q6: Are there industrial application examples?

The methods introduced in this series are actually used in a wide range of industries including petroleum refining, chemical plants, pharmaceuticals, and semiconductor manufacturing. Specific application examples are introduced in the "Practical Examples" section of each chapter.

Start Learning

🚀 When ready, start with Chapter 1

Chapter 1: Fundamentals of Time Series Data Analysis →

Overall Series Structure

Chapter	Title	Reading Time	Code Examples	Difficulty
Chapter 1	Fundamentals of Time Series Data Analysis	30-35 minutes	10	Intermediate
Chapter 2	Multivariate Process Data Analysis	30-35 minutes	10	Intermediate to Advanced
Chapter 3	Anomaly Detection and Diagnosis	30-35 minutes	10	Advanced
Chapter 4	Feature Engineering and Prediction Models	30-35 minutes	10	Advanced
Chapter 5	Real-time Data Analysis Systems	30-35 minutes	10	Advanced

References

Montgomery, D. C. (2019). Design and Analysis of Experiments (9th ed.). Wiley.
Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.
Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle III, F. J. (2016). Process Dynamics and Control (4th ed.). Wiley.
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). "A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code." Technometrics, 42(1), 55-61.

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