On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

Abstract

The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation.

Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education

“…More specifically, students verified the functionality of the two systems by creating water misuse events (Figure 9a) via repositioning the valves of the pumping system (Figure 9b), and monitoring the corresponding results on their mobile phone screens. The recorded results indicated that 9 over 10 predictions were correct, which is in line with the performance of each machine-learning system, as explained in [29,30].…”

“…To that end, this paper takes into consideration the material provided by two studies that use machine learning techniques for developing detection systems in order to address typical irrigation network problems. The first one introduces a water-misuse alert system [29], while the second one utilizes a classification model to detect water pump malfunctions in agricultural premises [30]. This article, except from providing a brief technical overview, is trying to explain how the latter systems can be transformed into effective educational instruments, suitable for serving the priorities of an agricultural engineering laboratory.…”

“…In more detail, the first step of this research, in the direction of the on-device intelligence deployment technique [28], was the development of a classification model executed on a microcontroller attached to a commercial faucet along with a flow sensor so as to determine water-consumption profiles and alert the user about them [29]. To further benefit from this deployment technique, a machine-learning model was developed for classifying and diagnosing possible motor defects in a water pump, using vibration data from an accelerometer to achieve a more comprehensive and precise perspective on the factors influencing the system [30]. These preexisting works, apart from introducing technical innovations, provide fertile ground, from an educational perspective, for pedagogical setup descriptions, experimentation, and evaluation reports that are among the main subjects of the study being presented.…”

Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)

“…ML has been widely adopted for prediction tasks in various domains, including healthcare [7], disease prevention [8], environmental science [9], and the energy sector itself [10,11]. In the industry sector, the use of ML is even more extensive for early fault detection and the condition monitoring of industrial equipment [12][13][14][15]. These studies showed that such techniques could improve equipment reliability, reduce downtime and maintenance costs, and increase operational efficiency.…”