Experimental study of the unsteady vibration signature for a Sirocco fan unit

Abstract

The small forward-curved blades known as Sirocco fan units are very common and widespread solution for air conditioning used in public transportation applications, as buses or trains. The users quietness and comfort have become a main concerns in the automotive industry. For such kind of turbomachinery flow , the patterns becomes always highly 3D and unsteady, compromising the referred comfort, and setting the focus on the working flow variables. A mathematically exact solution for that flow, which would provide any required information on pressure or forces, is out of scope at the current engineering design processes. Nevertheless, some flow features and mechanical data are needed to progress in the frame of a modern industrial environment, involving maintenance protocols with important temporal and economic constraints for different design procedures. The correctness of a given maintenance protocol relies on its feasibility to handle a set of machine working parameters or variables, including a number of them as wide as possible. Doing so, a set of not-dangerous ranges for them can be established. Such ranges are often defined promoting a series of failures similar to real ones, when the machine is in its operative lifetime. In this paper and in order to establish proper working ranges for maintenance purposes, a series of failures have been experimentally tested for a Sirocco fan unit. Initially, real data from industry have been required and a list of main failures was made, including (1) impeller or rotor unbalance, (2) impeller channel obstruction and (3) blocked inlet. The failures are studied using a purified orbit diagram (POD) technique and a symmetrized dot pattern (SDP) technique. All four working conditions are studied for at least three different flow rates and, therefore, a deeper insight into the fan working parameters and options are made feasible. In the frame of the maintenance protocol, a full set of ranges for the considered failures has been obtained. Therefore, the present paper shows a novel possibility to enhance existing maintenance protocol using two advanced frequency-based techniques.

A Novel Multi–Scale One–Dimensional Convolutional Neural Network for Intelligent Fault Diagnosis of Centrifugal Pumps

“…Among these methods, utilizing mechanical vibration data and data analysis for fault diagnosis demonstrates robustness and promising prospects. Fault diagnosis based on mechanical vibration data can be categorized into two types [6]. One involves establishing mathematical models for simulation verification, and many successful model-based methods have been developed and applied [7].…”