Equation types of deep models are often employed in fault diagnosis. Upon diagnosis this quantitative process knowledge is utilized as a criterion for satisfaction/violation in a Boolean or non-Boolean manner. Therefore, the resolution of equation-oriented fault diagnosis systems is often limited to, at most, fault isolation at a qualitative level. A deep model algorithm (DMA) is proposed to improve diagnostic resolution. First, tolerances of model equations are defined for each model equation with respect to each fault origin. Following the new definition of tolerance, degree of fault is defined to detect the level of fault and a consistency factor is used to evaluate the consistency given by different model equations. A CSTR example is used to illustrate the resolution of DMA. Results show that the proposed method is effective in identifying fault origins.