Prognosis of Rotor Parts Fly-off Based on Cascade Classification and Online Prediction Ability Index

Large rotating machines, e.g., compressors, steam turbines, gas turbines, are critical equipment in many process industries such as energy, chemical, and power generation. Due to high rotating speed and tremendous momentum of the rotor, the centrifugal force may lead to flying apart of the rotor parts, which brings a great threat to the operation safety. Early detection and prediction of potential failures could prevent the catastrophic plant downtime and economic loss. In this paper, we divide the operational states of a rotating machine into normal, risky, and high-risk ones based on the time to the moment of failure. Then a cascade classifying algorithm is proposed to predict the states in two steps, first we judge whether the machine is in normal or abnormal condition; for time periods which are predicted as abnormal we further classify them into risky or high-risk states. Moreover, traditional classification model evaluation metrics, such as confusion matrix, true-false accuracy, are static and neglect the online prediction dynamics and uneven wrong-prediction prices. An Online Prediction Ability Index (OPAI) is proposed to select prediction models with consistent online predictions and smaller close-to-downtime prediction errors. Real-world data sets and computational experiments are used to verify the effectiveness of proposed methods.

Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

Enhancing Generalizability of Predictive Models with Synergy of Data and Physics

Wind farm needs prediction models for predictive maintenance. There is a need to predict values of non-observable parameters beyond ranges reflected in available data. A prediction model developed for one machine many not perform well in another similar machine. This is usually due to lack of generalizability of data-driven models. To increase generalizability of predictive models, this research integrates the data mining with first-principle knowledge. Physics-based principles are combined with machine learning algorithms through feature engineering, strong rules and divide-and-conquer. The proposed synergy concept is illustrated with the wind turbine blade icing prediction and achieves significant prediction accuracy across different turbines. The proposed process is widely accepted by wind energy predictive maintenance practitioners because of its simplicity and efficiency. Furthermore, this paper demonstrates the importance of embedding physical principles within the machine learning process, and also highlight an important point that the need for more complex machine learning algorithms in industrial big data mining is often much less than it is in other applications, making it essential to incorporate physics and follow Less is More philosophy.