The existing Data Quality Assessment （DQA） methods often only analyze the basic concept of a specific Data Quality Dimension （DQD）， ignoring the influence of fine-grained sub-dimensions that reflect key information of Data Quality （DQ） on the assessment results. To address these problems， an Industrial Multivariate Time Series Data Quality Assessment （IMTSDQA） method was proposed. Firstly， the DQDs to be evaluated such as completeness， normativeness， consistency， uniqueness， and accuracy were fine-grainedly divided， and the correlation of the sub-dimensions within the same DQD or between different DQDs was considered to determine the measurements of these sub-dimensions. Secondly， the sub-dimensions of attribute completeness， record completeness， numerical completeness， type normativeness， precision normativeness， sequential consistency， logical consistency， attribute uniqueness， record uniqueness， range accuracy， and numerical accuracy were weighted to fully mine the deep-level information of DQDs， so as to obtain the evaluation results reflecting the details of DQ. Experimental results show that compared to existing approaches based on qualitative analysis of frameworks and model construction according to basic definitions of DQDs， the proposed method can assess DQ more effectively and comprehensively， and the assessment results of different DQDs can reflect DQ problems more objectively and accurately.