The growing complexity of data derived from Industrial Internet of Things (IIoT) systems presents substantial challenges for traditional machine-learning techniques, which struggle to effectively manage the needs of predictive maintenance applications. Automated machine-learning (AutoML) techniques present a promising solution by streamlining the machine-learning process, reducing the necessity for manual hyperparameter tuning and computational resources, thereby positioning themselves as a potentially transformative innovation in the Industry 4.0 era. This research introduces two distinct models: AutoML, employing PyCaret, and Auto Deep Neural Network (AutoDNN), utilizing AutoKeras, both aimed at accurately identifying various types of faults in ball bearings. The proposed models were evaluated using the Case Western Reserve University (CWRU) bearing faults dataset, and the results showed a notable performance in terms of achieving high accuracy, recall, precision, and F1 score on the testing and validation sets. Compared to recent studies, the proposed AutoML models demonstrated superior performance, surpassing alternative approaches even when they utilized a larger number of features, thus highlighting the effectiveness of the proposed methodology. This research offers valuable insights for those interested in harnessing the potential of AutoML techniques in IIoT applications, with implications for industries such as manufacturing and energy. By automating the machine-learning process, AutoML models can help decrease the time and cost related to predictive maintenance, which is crucial for industries where unplanned downtime can lead to substantial financial losses.

As industrial processes are becoming increasingly complex and data-driven, the need for accurate quality predictions in manufacturing systems is regarded as critical. To address this challenge, AE-BiLA (Autoencoder-Bidirectional Long Short-Term Memory with Attention mechanism) has been proposed as a framework in which a stacked Long Short-Term Memory (LSTM) autoencoder is combined with a bidirectional LSTM enhanced by an Attention mechanism for predicting quality in multi-stage manufacturing processes (MMP). First, high-dimensional, noisy data are reduced by employing the stacked LSTM autoencoder, with essential information being preserved. Next, the compressed features are fed into the bidirectional LSTM, where significant temporal patterns are highlighted by the Attention mechanism. The method was validated on a real-world MMP dataset. An R² (coefficient of determination) of 0.9452 was obtained in Stage 1, demonstrating that upstream process dynamics were effectively captured. In contrast, an R² of 0.7329 was produced in Stage 2, reflecting increased complexity and variability in downstream operations. Moreover, the Symmetric Mean Absolute Percentage Error (SMAPE) in Stage 2 was reduced to 1.9319 from 19.3583 in Stage 1, thereby underscoring that outliers and noise were successfully managed. Overall, the AE-BiLA framework outperforms existing methods by effectively integrating denoising with a bidirectional recurrent structure. Despite the increased computational overhead, it is expected that the framework will yield substantial gains in productivity, lower waste levels, and reduce operational costs.