Predictive maintenance is a promising approach to increase safety and productivity in chemical plants. One notoriously difficult problem in predictive maintenance are hard to predetermine, non-deterministic changes such as drifts. The term "drift" can be found with different definitions in this context. Therefore, it is defined here as changes in variables and parameters that occur orders of magnitude slower than the nominal process dynamics and are not directly measurable. Previous research resulted in a hybrid method that detects and diagnoses drifts from two sources: process and equipment. This method combines model-based and statistical approaches and additional information from the equipment, such as measurement gain or power consumption, is envisioned to reduce uncertainty about the drift cause [1]. First case studies revealed significant problems regarding economically viable integration of additional information. These problems arise due to the amount of information in scenarios with multiple devices, making analysis costly and time-consuming. For solving these problems, an automated evaluation of the information is introduced. It analyzes which additional equipment information is most relevant to distinguish between different drifts, based on sensitivity analyses. Additionally, a drift index - a unique mathematical label for drifts - is defined. The extended method is then successfully applied to a scenario closer to the reality in chemical plants with multiple adjacent devices. It is shown that the additional information is evaluated automatically and drifts are diagnosed more efficiently than in the previous method.