The invention relates to the technical field of detection methods, in particular to a roller kiln energy consumption anomaly detection method based on self-adaptive principal component analysis. The roller kiln energy consumption anomaly detection method comprises the following specific steps that (1) firstly, a sample L is collected, the collected sample is standardized, and then a covariance matrix S is calculated; (2) characteristic decomposition is carried out on S, a principal component number k is obtained through calculation by using a CPV method, and a characteristic vector and a characteristic value are intercepted to obtain a load matrix P and a characteristic value matrix lambda; (3) an initial control limit is calculated; (4) a new sample of the sample L is continuously collected after a certain moment, standardizing is carried out, and abnormal judgment is carried out on the new sample; and (5) the steps (1), (2), (3) and (4) are circulated until the number of the samplesneeding to be updated reaches the number of the samples needing to be updated, the samples are updated and abnormal information judgment is carried out. According to the roller kiln energy consumptionanomaly detection method based on the self-adaptive principal component analysis, the abnormal condition of the roller kiln can be effectively detected, and the detection efficiency is greatly improved.