Fault Detection and Diagnosis (FDD) has gained significant interest in recent days among advanced industrial automation. Sustaining operational consistency and avoiding downtime in industrial systems necessitate sophisticated FDD. Conventionally, industrial fault diagnosis used model-driven method based on specialist knowledge and manual features. The common application of machine learning (ML) technique has completed it probable to substitute these conventional methods with more effective solutions able to timely fault detection by employing huge number of sensory information. Over the latest years, deep learning (DL) approach have been leveraged to fault diagnosis because of their effective feature representation capabilities, bringing novel opportunities to the advancement of fault detection. This study proposes a Quantum Computing Enhanced Deep Representation Learning for Multi-Label Industrial Fault Detection and Classification (QCEDRL-MIFDC) methodology. The purpose of the proposed model is to detect and classify faults in industrial systems using quantum computing approaches. In this initial stage, data preprocessing is performed through encoding, outlier removal and data normalization to ensure high-quality data for efficient learning. Moreover, the model employs a hybrid feature selection strategy combining LASSO, Elastic Net, Adaptive LASSO, and Group LASSO to identify the most relevant and informative features. For fault detection, a quantum variational autoencoder-transformer (QVAET) model is utilized to classify four types of faults: no fault, overheating, leakage, and power fluctuation faults. Finally, model training is carried out using the Nadam optimizer to efficiently update parameters and accelerate convergence. The experimental analysis of the QCEDRL-MIFDC model is performed using benchmark Industrial Fault Detection Dataset. Extensive comparative results report the promising performance of the QCEDRL-MIFDC model over recent state of art techniques.