The ascent of Industry 4.0 and smart manufacturing has emphasized the use of intelligent manufacturing techniques, tools, and methods such as predictive maintenance. The predictive maintenance function facilitates the early detection of faults and errors in machinery before they reach critical stages. This study suggests the design of an experimental predictive maintenance framework, for conveyor motors, that efficiently detects a conveyor system’s impairments and considerably reduces the risk of incorrect faults diagnosis in the plant; We achieve this remarkable task by developing a machine learning model that classifies whether the abnormalities observed are production-threatening or not. We build a classification model using a combination of time-series imaging and convolutional neural network (CNN) for better accuracy. In this research, time-series represent different observations recorded from the machine over time. Our framework is designed to accommodate both univariate and multivariate time-series as inputs of the model, offering more flexibility to prepare for an Industry 4.0 environment. Because multivariate time-series are challenging to manipulate and visualize, we apply a feature extraction approach called principal component analysis (PCA) to reduce their dimensions to a maximum of two channels. The time-series are encoded into images via the Gramian Angular Field (GAF) method and used as inputs to a CNN model. We added a parameterized rectifier linear unit (PReLU) activation function option to the CNN model to improve the performance of more extensive networks. All the features listed added together contribute to the creation of a robust future proof predictive maintenance framework. The experimental results achieved in this study show the advantages of our predictive maintenance framework over traditional classification approaches.

Throughout industrial revolutions, equipment downtime mitigations have been one of the ultimate goals of most factories. Several tools, such as human machine interface (HMI) alarming systems or predictive maintenance schedules, assist in reducing system downtime but still depend on the operators’ ability to swiftly retrieve, understand, and efficiently act upon reported failures. We propose the design of a hybrid experimental artificial intelligence (AI) and generative AI chatbot HMI that effectively extracts factory equipment conditions that are useful for troubleshooting and predictive maintenance analysis. We achieve these functions by feeding experimental factory-monitored data to the customized chatbot application tool running in its back-end, a Langchain agent linked to the OpenAI GPT $$-$$- 3.5 language model (LM) via OpenAI APIs. We design our chatbot front-end with Streamlit, an open-source web app. In the context of I5.0, our chatbot HMI uses personalized natural language, English, to interact with the operator, making the information extraction more understandable. We also integrate the generative AI capability of the GPT 3.5 LM that augments the factory data based on the loaded format to create a larger dataset for additional tasks like machine learning modelling. The experimental results show the accuracy of our customized chatbot HMI when retrieving data based on specific prompts and the advantages of a reduced troubleshooting time compared to operations in traditional factories, which are highly dependent on supervisors’ interventions. Our study provides a valuable example of upgrading standard factory HMIs to I5.0-capable ones by implementing customized AI and generative AI chatbots within operational industrial environments.

The Industrial Internet of things (IIoT), the implementation of IoT in the industrial sector, requires a deterministic, real-time, and low-latency communication response for its time-critical applications. A delayed response in such applications could be life-threatening or result in significant losses for manufacturing plants. Although several measures in the likes of predictive maintenance are being put in place to prevent errors and guarantee high network availability, unforeseen failures of physical components are almost inevitable. Our research contribution is to design an efficient communication prototype, entirely based on internet protocol (IP) that combines state-of-the-art communication computing technologies principles to deliver a more stable industrial communication network. We use time-sensitive networking (TSN) and edge computing to increase the determinism of IIoT networks, and we reduce latency with zero-loss redundancy protocols that ensure the sustainability of IIoT networks with smooth recovery in case of unplanned outages. Combining these technologies altogether brings more effectiveness to communication networks than implementing standalone systems. Our study results develop two experimental IP-based industrial network communication prototypes in an intra-domain transmission scenario: the first one is based on the parallel zero-loss redundancy protocol (PRP) and the second one using the high-availability seamless zero-loss redundancy protocol (HSR). We also highlight the benefits of utilizing our communication prototypes to build robust industrial IP communication networks with high network availability and low latency as opposed to conventional communication networks running on seldom redundancy protocols such as Media Redundancy Protocol (MRP) or Rapid Spanning Tree Protocol (RSTP) with single-point of failure and delayed recovery time. While our two network communication prototypes—HSR and PRP—offer zero-loss recovery time in case of a single network failure, our PRP communication prototype goes a step further by providing an effective redundancy scheme against multiple link failures.