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Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

# Stall Warning in a Low-Speed Axial Fan by Visualization of Sound Signals

[+] Author and Article Information
Anthony G. Sheard

Fläkt Woods Limited Axial Way, Colchester CO4 5AR, UK

Alessandro Corsini, Stefano Bianchi

Dipartimento di Meccanica e Aeronautica Sapienza, University of Rome, Via Eudossiana 18, I-00184 Rome, Italy

J. Eng. Gas Turbines Power 133(4), 041601 (Nov 23, 2010) (10 pages) doi:10.1115/1.4002178 History: Received May 16, 2010; Revised May 31, 2010; Published November 23, 2010; Online November 23, 2010

## Abstract

This study describes the development of a novel stall-detection methodology for low-speed axial-flow fans. Because aerodynamic stall is a major potential cause of mechanical failure in axial fans, effective stall-detection techniques have had wide application for many years. However, aerodynamic stall does not always result in mechanical failure. A subsonic fan can sometimes operate at low speeds in an aerodynamically stalled condition without incurring mechanical failure. To differentiate between aerodynamic stall conditions that constitute a mechanical risk and those that do not, the stall-detection methodology in the present study utilizes a symmetrized dot pattern (SDP) technique that is capable of differentiating between stall conditions. This paper describes a stall-detections criterion based on a SDP visual waveform analysis and develops a stall-warning methodology based on that analysis. This study presents an analysis of measured acoustic and structural data across nine aerodynamic operating conditions represented in a $3×3$ matrix. The matrix is a combination of (i) three speeds (full-, half-, and quarter-speed) and (ii) three operational states (stable operation, incipient stall, and rotating stall). The matrix of SDPs and structural data are used to differentiate critical stall conditions (those that will lead to mechanical failure of the fan) from noncritical ones (those that will not result in mechanical failure), thus providing a basis for an intelligent stall-warning methodology.

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## Figures

Figure 1

Sectional view of the impeller with the microphones mounted flush with the casing walls

Figure 2

Phase angle of the frequency response on pressure measurements

Figure 3

Performance map and operating regions

Figure 4

Schematic diagram of technique for plotting SDP

Figure 5

SDPs from sine waves: (a) f=288 Hz, (b) f=144 Hz, and (c) white noise after Shibata (2000) (15)

Figure 6

SDPs of one axis segment (Θ=0°) for different (a) lag L and (b) angular gain ξ during 240 rotor revolutions

Figure 7

SDP variation with the frequency sampling at 100% speed and Φ=0.13 sampled at (a) 50 kHz, (b) 5 kHz, and (c) 1 kHz

Figure 8

SDP variation with the sampling time measures at different shaft speeds (L=30, ξ=20 deg, sampled at 50 kHz and 100% speed)

Figure 9

Fan map and investigated operating points

Figure 10

SDPs of the case-wall pressure signals of the tested fan in different operative conditions and rotational speed

Figure 11

SDPs of the case-wall pressure signals of the tested fan in different operative conditions and rotational speed

Figure 12

Superposition of normal operation and stall incipient SDPs at 100% rotor speed

Figure 13

SDP at 100%

Figure 14

Set up of measurement chain for the actual fan diagnostic system using the SDP technique

Figure 15

Template matching of visual dot patterns in stall diagnosis

Figure 16

The curve of best fit though material test data is referred to as the Gerber line

Figure 17

The Gerber Line associated with material test data, and the Gerber line with a safety factor of 2

Figure 18

The Gerber line associated with full-speed normal and half-speed stalled operation

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