This two-part 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 sub-sonic 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 utilises a symmetrised dot pattern (SDP) technique that is capable of differentiating between critical and non-critical conditions. The SDP for a stall condition is different from that for a non-stall condition providing, a basis for differentiation of the two. Part I of this study presented the azimuthal experimental data which established the stall characteristics of a variable-speed fan. Part II describes a stall-warning criterion based on an SDP visual waveform analysis and developed stall-detection methodology based on that analysis. The study presents an analysis of the acoustic and structural data across the nine aerodynamic operating conditions represented in a 3 x 3 matrix combination of: (i) three speeds (full-, half-, and quarter-speed) and (ii) three operational states (stable operation, incipient stall and rotating stall). This differentiates critical stall conditions (those that will lead to mechanical failure of the fan) from non-critical ones (those that will not result in mechanical failure), thus providing a basis for an intelligent stall-detection methodology.
