The invention discloses a
loader working condition identification model construction and identification method. Firstly, a corresponding sensor is arranged on the
loader to collect multi-source signals such as torque, pressure, gear and
brake, and the like, and the data is standardized, the zero floating
signal is stripped, the missing value is interpolated and compensated, and the collected
signal is processed by
noise reduction filter. Secondly,
principal component analysis is used to select the feature attributes from the
loader's multi-attribute data, and
statistical analysis is used to extract the feature of the principal component. Then, the loader working condition samples are established, and the
association mapping between the load
signal and the pre-classified working condition patterns is established by using the
supervised learning data mining algorithm, and the working condition identification model is formed by training a large number of data samples. Combining the
feature extraction method of
principal component analysis with KNN
algorithm, the distance formula of KNN
algorithm is improved to make it more consistent with the working condition identification, and theaccuracy and efficiency of the working condition identification algorithm are improved.