The beer industry is a significant global market, and the number of small breweries is on the rise. Centrifugal pumps are vital to the production system, especially in microbreweries. Failures in the machine setup, such as incorrect valve positions or blockages in the piping system, can result in increased production time and energy consumption, the need for rework, and even impact the quality of the beer. In this context, the correct operation of the pump can be monitored, and faults detected early through Intelligent Fault Diagnosis (IFD). Since microbreweries typically have a low level of automation and limited resources for investment, this article aims to develop an IFD capable of detecting blockages and inlet problems in centrifugal pumps by exploring a soft sensor approach. The predictive model is constructed based on data provided by centrifugal pump drives, such as current, torque, and power factor, and does not require additional sensors. This data is collected through a managed switch with a mirrored port, captured with Wireshark, and interpreted by a Python script that extracts univariate statistical features. A dataset containing 1260 samples was created, covering data from healthy operation, closed pump inlet, and closed pump outlet. With this dataset, predictive models were trained using SVM and ANN to identify the pump's operating condition. Performance analysis was conducted using accuracy and precision metrics. The results of this study are promising, indicating that the proposed IFD approach can be effectively utilized in developing sensors specifically designed for Industry 4.0 applications. This approach enhances automation and predictive maintenance capabilities, making it particularly beneficial for small-scale breweries looking to optimize their production processes and maintain high-quality standards.