Machine fault diagnosis is essentially an issue of pattern recognition, which heavily
depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular
unsupervised neural network, has been used for failure detection but with two limitations: needing
predefined static architecture and lacking ability for the representation of hierarchical relations in
the data. This paper presents a novel study on failure detection of gearbox using the Growing
Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical
architecture composed of independent growing SOMs. The GHSOM can adapt its architecture
during unsupervised training process and provide a global orientation in the individual layers of the
hierarchy; hence the original data structure can be described correctly for machine faults diagnosis.
Gearbox vibration signals measured under different operating conditions are analyzed using the
proposed technique. The results prove that the hierarchical relations in the gearbox failure data can
be intuitively represented, and inherent structure can be unfolded. Then gearbox operating
conditions including normal, tooth cracked and tooth broken are classified and recognized clearly.
The study confirms that GHSOM is very useful and effective for pattern recognition in mechanical
fault diagnosis, and provides a good potential for application in practice.
