Dynamometer cards are commonly used to
analyze down-hole working conditions of pumping systems
in actual oil production. Nowadays, the traditional supervised
learning methods heavily rely on the classification
accuracy of the training samples. In order to reduce the
errors of manual classification, an automatic clustering
algorithm is proposed and applied to diagnose down-hole
conditions of pumping systems. The spectral clustering
(SC) is a new clustering algorithm, which is suitable for
any data distribution. However, it is sensitive to initial
cluster centers and scale parameters, and needs to predefine
the cluster number. In order to overcome these shortcomings,
we propose an automatic clustering algorithm, fast
black hole–spectral clustering (FBH–SC). The FBH algorithm
is used to replace the K-mean method in SC, and a
CritC index function is used as the target function to
automatically choose the best scale parameter and clustering
number in the clustering process. Different simulation
experiments were designed to define the relationship
among scale parameter, clustering number, CritC index
value, and clustering accuracy. Finally, an example is
given to validate the effectiveness of the proposed
algorithm.