Accurate fault classification in rotating machinery under changing speeds and loads is a critical challenge in industrial predictive maintenance, where vibration signatures shift across operating regimes and black-box decisions are difficult to trust. This paper presents a hybrid architecture that combines Transformers with Logic Tensor Networks (LTNs), used here as the neuro-symbolic learning framework because they ground first-order rules into differentiable satisfiability terms optimized directly in the training objective, for fault diagnosis on two public benchmarks: the Drivetrain Dynamics Simulator (DDS) (multiple speed/load regimes) and the University of Connecticut (UoC) gear-fault dataset. A compact 1-D Transformer encodes raw vibration windows, and an LTN layer imposes soft first-order constraints during training. We introduce a dynamic rule module that induces, merges, and prunes centroid-based similarity rules as the embedding geometry evolves, enabling the constraint set to adapt to within-class variability. Unlike prior LTN-based approaches such as LogicLSTM, which reweight a fixed rule set, our rules are induced and updated dynamically during training. Experiments show improvements over strong neural and neuro-symbolic baselines on DDS (average accuracy 94.01% vs 88.20%), and gains over a strong Transformer baseline on UoC (macro F1 0.939). Beyond accuracy, the induced rules provide compact, queryable explanations by identifying prototypical vibration-window patterns that support a prediction. Confidence calibration improves versus baselines under the same evaluation protocol. Because LTN supervision acts only during training, inference latency matches the base Transformer. The results support neuro-symbolic fusion as a practical path to accurate and explainable fault diagnosis under varying operating conditions.