## 4 Applications of Intelligent Acoustic Monitoring
### Industry and Predictive Maintenance
The system was built on a lightweight, on-board single-board computer and tested in realistic flight conditions, including both hovering and intensive manoeuvres. An extensive analysis of the effectiveness of models based on convolutional networks and LSTM was conducted, with the latter proving to be more computationally efficient, achieving over 98% accuracy in classifying faults based on 200 ms samples. The authors were the first to present a comprehensive solution for acoustic FDI that operated with on-board signal recording, significantly bringing it closer to practical use. In the future, they plan to further optimise the algorithm for real-time operation, extend the system to detect other types of faults (for example, engine bearings), and integrate data from vibration and acoustic sensors to increase detection reliability. In [125], [PERSON] et al. developed a method for diagnosing faults in mine fans based on the analysis of acoustic signals. The authors proposed a four-element expert system that covered the acquisition and denoising of sound signals, feature extraction, recognition of the operating state, and fault diagnosis. As part of the work, an analysis of the sources of fan noise was conducted, utilising wavelet transformation and wavelet packets for denoising and extracting characteristic signal features. To identify faults, the authors generated artificial sound signals corresponding to three failure scenarios (bearing overheating, bearing damage, and drive belt problem). The results showed that different failure states were characterised by clearly different feature vectors, allowing for their effective differentiation. The evaluation of the method indicated its great potential as an inexpensive, non-contact tool for 24/7 monitoring of the technical condition of fans in difficult mine conditions. In [126], [PERSON] et al. presented a lightweight and effective method for the early detection of brushless motor faults in unmanned aerial vehicles (UAVs), based on sound analysis and feature extraction using the MFCC method, followed by classification using the SVM algorithm. In that study, four UAV models were built--a helicopter, a duocopter, a tricopter, and a quadcopter--and sound data from the motors in a healthy state and with different types of faults (imbalance, magnet, propeller, and bearing damage) were collected. The method achieved very high classification efficiency--from 90.53% to 100%, with the highest accuracy obtained in single- and twin-motor models. The key advantage of the proposed approach was its low computational complexity, which enabled it to be applied in real time on embedded systems, distinguishing it from more resource-intensive solutions based on deep learning. The work is valuable for its practical application and extensive evaluation of system performance on various UAV configurations. In [127], [PERSON] et al. proposed an intelligent device for the early detection of mechanical faults in vehicles, based on the analysis of acoustic and vibration signals recorded inside the car. The system, equipped with a microphone, vibration sensors, and a microcontroller, used a Kalman filter to reduce noise and MFCCs to analyse the characteristics of the engine sound, whose changes may indicate a failure. The presented results indicated that the proposed solution effectively identified mechanical problems at an early stage, thereby reducing the risk of road accidents. The advantage of the work lay in the combination of the simplicity of implementation with a real-world application, as well as its openness to future extensions. The authors assumed the integration of the system with VANET networks was possible to monitor the technical condition of vehicles in real time. Their practical approach makes a valuable contribution to the development of intelligent road safety systems. Acoustic diagnostic systems are becoming a real alternative to more complex and expensive solutions, offering good accuracy even in harsh industrial conditions.