<p>Given the volatile operational environments, rotating machinery is subject to a high rate of failures and expensive maintenance costs. This calls for the imperative undertaking of formulating innovative methodologies for diagnosing faults in rotating machinery. Industrial fault diagnosis faces two prevailing challenges in the real world: fusing one-dimensional vibration signals from two different perspectives and assessing fault severity. We employ a two-branch network for processing one-dimensional vibration signals from both time and frequency domains. Additionally, we incorporate a unimodal binomial distribution and a weighted cross-entropy loss to account for fault severity. Amalgamating ordinal regression with two-branch networks, a model termed the ordinal two-branch networks with a unimodal binomial distribution (OTBN-UBD) is developed for fault severity classification. We validate the efficacy of the OTBN-UBD model by using the failure case data collected from real industrial rotating machinery in operation. The OTBN-UBD model, incorporating ordinal information, shows superior multi-classification and ordinal regression performance, as indicated by our experimental results. In practical engineering applications, the OTBN-UBD model demonstrates considerable flexibility, with the potential to mitigate extreme misclassifications through adjusting weights within the loss.</p>