This research addresses the challenge of monitoring large-scale machine halls, particularly in the context of iron making processes. We propose an acoustic sound-based condition monitoring (ASCM) system to detect potential faults and damages in machinery. The study focuses on selecting suitable audio features, integrating physical insights regarding the fault, and determining optimal window lengths for feature extraction. Our fault detection method utilizes outlier detection with a Gaussian mixture model trained on features extracted only from normal operating conditions. We compare conventional audio features with physically motivated features and conduct a window length analysis. The results demonstrate the effectiveness of our approach and the impact of incorporating physically motivated features for fault detection performance.

Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this paper, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a General Likelihood Ratio Test as a detector. As demonstrated in this paper, by accurately describing the fault pattern based on a small amount of fault data,very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.

A SAW-based temperature sensor system is presented. Much effort was placed in the development of a high temperature resistive transponder compromising of both a SAW device and an antenna stable up to 400 /spl deg/C for at least a few days. The system solution also includes a low-cost reader unit employing the FSCW (frequency stepped continuous wave) principle operating in the ISM band from 2.4 GHz to 2.4835 GHz. Spare space on the SAW device has been employed also to implement an ID for identification purpose. Hence, this solution also allows for tagging of objects exposed to harsh environments.

In powder processing industries, information about the mass concentration and the moisture content of the material is often required for process monitoring and control. Capacitive sensors are widely applied for measurements of powder materials, as they are sensitive to variations in mass concentration and changes in moisture content. However, for processes where the mass concentration and the moisture content of the material can vary, it is not possible to distinguish between the two effects based on capacitive measurements alone. This paper shows how permittivity and conductivity measurements can be used to independently determine the mass concentration and the moisture content of a powder. The proposed method is demonstrated for an exemplary coal powder. A dielectric characterization based on impedance measurements of a coaxial probe is carried out. Based on these measurement experiments, a modelling approach is presented that allows the independent determination of the mass concentration and the moisture content of powders, which is of great interest for the development of measurement systems for powder processing industries.