Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions.

Area of Science:

• Manufacturing Engineering
• Industrial Systems
• Reliability Engineering

Background:

• Industrial prognostics and health management (PHM) development has historically lagged behind academic research due to practical implementation challenges.
• Existing PHM solutions often face hurdles in manufacturing environments, including data quality issues and the need to model systems with trend-based degradation.

Purpose of the Study:

• To propose a novel framework for the initial development of industrial PHM solutions.
• To provide methodologies for critical planning and design stages in industrial PHM.
• To address and offer solutions for inherent challenges in manufacturing health modeling.

Main Methods:

• Adaptation of the system development life cycle (SDLC) commonly used for software applications to industrial PHM development.
• Presentation of specific methodologies for the planning and design phases of industrial PHM solutions.
• Identification of data quality and trend-based degradation as key challenges, with proposed methods to overcome them.

Main Results:

• A structured framework for developing industrial PHM solutions is presented.
• Effective methodologies for planning, design, and addressing data quality and degradation modeling are detailed.
• A case study on a hyper compressor at The Dow Chemical Company demonstrates the framework's practical value.

Conclusions:

• The proposed SDLC-based framework facilitates the development of industrial PHM solutions.
• The framework effectively addresses practical challenges such as data quality and system degradation modeling.
• The case study validates the framework's utility and provides guidelines for broader application in manufacturing.