Intelligent predictive maintenance (IPdM) is a maintenance strategy that makes maintenance decisions automatically and dynamically based on Artificial Intelligence and Data mining techniques through condition monitoring of machines, equipment and production processes. IPdM system consists of the following main modules: sensor and data acquisition, signal and data processing, feature extractions, maintenance decision-making, key performance indicators, maintenance scheduling optimization and feedback control and compensation. Among them, the most important part of IPdM is maintenance decision-making, which includes diagnostics and prognostics. This paper proposes a framework of intelligent faults diagnosis and prognosis system (IFDaPS) and discuss some key technologies for implement IPdM policy in manufacturing and industries. A case study focus on the vibration signals collected from the sensors mounted on a pressure blower for critical components monitoring. We decompose the pre-processed signals into several signals using Wavelet Packet Decomposition (WPD), and then the signals are transformed to frequency domain using Fast Fourier Transform (FFT). The features extracted from frequency domain are used to train Artificial Neural Network (ANN). Trained ANN model is able to identify the fault of the components and predict its Remaining Useful Life (RUL). The case study demonstrates how to implement the proposed framework and intelligent technologies for IPdM and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

With more and more governments and organizations taking Carbon Footprint as the measure of greenhouse gas emission, the study about the calculation of carbon footprint has become a hot spot. The paper analyzed the carbon footprint in different stages of a product life circle, including manufacturing, transporting, using and disposing and also studied the part contributing the largest carbon emission. Especially in the calculation of carbon emission of manufacturing stage, recursive call algorithm was applied. The optimization design model of carbon footprint was also depicted. All the work this paper had undertaken facilitates to formulate specific carbon emission reduction measures.

Wind power is emerging as a particularly attractive form of renewable energy. The predomination of Dielectric Electric Active Polymer (DEAP) has been shown to operate in transforming mechanical strain energy to electrical energy as generator mode. Their characteristics make them potentially well suited for wind power takeoff systems. In this article, a novel DEAP micro generator is successfully developed about mechanical-electro energy conversion model. The proposed energy harvesting is based on capacity change induced by the mechanical strain. With the Mooney-Rivlin model, the theoretical modeling of energy harvesting cycle are analyzed. To verify the theoretical analysis, the prototype has been set up on the DEAP wind power micro-generator in this work. Many experiments were performed to verify the usability of the proposed DEAP generator method. These experimental investigations coincide with the energy conversion theory analytical model. The DEAPs have been proved to provide electrical energy with density as high as 1.5J.g-1.This value is much higher compared with the density of piezoelectric polymer (0.3J.g-1). The work will push forward the practical use of wave power for supplying general electrical needs, and supply theoretical foundation for potential applications such as ocean wave power.

3D vision based quality inspection has been widely applied in manufacturing industry. Product quality is retrieved from the point cloud obtained using 3D vision methods. Generally, three sorts of quality inspection methods can be selected according to the specific requirements. This paper studied a combining quality inspection method for the quality inspection of a plastic molded part with multiple geometry shapes. Only incomplete point cloud is available because of the characteristics of the part material. Shape fitting and template matching methods are applied for deformation detection with respect to different shapes. Experiment result shows the proposed method can accomplish the quality inspection task for the part with multiple geometry shapes.

Looking at the high rates of production and the steep competition in the world market, it becomes quite essential that the fault control is done in a very efficient way. This article presents a summary on the maintenance, the monitoring techniques, and the diagnosis methods for the condition based maintenance of machine tools. The paper initially gives a brief introduction on the condition based maintenance of machine tools. In the next part, the various methods for the monitoring are discussed followed by the models for data mining. The paper concludes that most of the techniques have their own advantages and drawbacks, so a careful selection of the techniques is needed to form a proper monitoring system.