With the development of equipment condition monitoring

especially wireless sensor networks

there are a number of measuring points and huge amount of data need to be transmitted

which can be solved by the compression algorithms.Traditional wavelet compression algorithms are linear transform and spend long computing time.They can not be used to compress data effectively

when there are high real-time requirement and nonlinear characteristics.The morphological Haar wavelet is simple and non-linear

but it just takes the numerical maximum and minimum values

which limits the accuracy of the data recovery.The morphological median wavelet is used to achieve the self-adaptation of the updating operator based on the local morphological feature of the signal.In this paper the morphological median wavelet is applied to compress the monitoring data

and the compression results are better than the morphological Haar wavelet.