An optimal scheduling and control method for a multiple pump system is proposed from the energy efficient point of view. The model-based optimal problem is first formulated and then converted to be a mixed integer nonlinear programming problem. The proposed method provides an optimal solution regarding to how many and which available pumps should be put into operation when the (head) demand to the system and/or system operating condition changes. The running speeds of operating pumps are also derived by the proposed algorithm. A feedback control mechanism is also introduced into the considered framework in order to enhance the system tracking performance and robustness. A nonlinear programming solution is derived and implemented in a testing facility. The experimental results show a clear and huge potential to improve multi-pump system's efficiency using the proposed algorithm and framework.

The control objective of a water boosting system equipped with multiple variable-speed pumps in parallel is to minimize the pump system energy consumption by control the number of running pumps and their corresponding speeds in a real-time manner, subject to potential changes of (system head) set-points and operating conditions. After a number of static models for different pump combinations are derived, a number of optimal scheduling algorithms are proposed from a formulated Mixed Integer Non-Linear Program (MINLP) problem. The Branch and Bound method is employed to cope with the considered MINLP problem and the Lagrangian Multipliers method is used to handle the corresponding nonlinear programming within each iteration. In order to cope with potential modeling errors, a feedback control mechanism is introduced into the proposed framework. In case of unknown operating conditions, an identification algorithm is proposed to estimate unknown system coefficients in an online manner. The experimental results show a huge potential to improve the energy efficiency of multi-pump systems using the proposed method and algorithms.

No abstract

This paper investigates the fault detection and diagnosis for a class of rolling-element bearings using signal-based methods based on the motor's vibration and phase current measurements, respectively. The envelope detection method is employed to preprocess the measured vibration data before the FFT algorithm is used for vibration analysis. The average of a set of Short-Time FFT (STFFT) is used for the current spectrum analysis. A set of fault scenarios, including single and multiple point-defects as well as generalized roughness conditions, are designed and tested under different operational conditions, including different motor speeds, different load conditions and samples from different operating time intervals. The experimental results show the powerful capability of vibration analysis in the bearing point-defect fault diagnosis under stationary operation. The current analysis showed a subtle capability in diagnosis of pointdefect faults depending on the type of fault, severity of the fault and operational condition. The generalized roughness fault can not be detected by the proposed frequency methods. The temporal features of the considered faults and their impact on the diagnosis analysis are also investigated.

No abstract