In-Line Measurement of the Surface Texture of Rolls Using Long Slender Piezoresistive Microprobes

Long slender piezoresistive silicon microprobes are a new type of sensor for measurement of surface roughness. Their advantage is the ability to measure at speeds of up to 15 mm/s, which is much faster than conventional stylus probes. The drawbacks are their small measurement range and tendency to break easily when deflected by more than the allowed range of 1 mm. In this article, previously developed microprobes were tested in the laboratory to evaluate their metrological properties, then tested under industrial conditions. There are several industrial measurement applications in which microprobes are useful. Measurement of the roughness of paper machine rolls was selected for testing in this study. The integration of a microprobe into an existing roll measurement device is presented together with the measurement results. The results are promising, indicating that measurements using a microprobe can give useful data on the grinding process.

Industrial Vertical Stirred Mills Screw Liner Wear Profile Compared to Discrete Element Method Simulations

Vertical stirred mills have been widely applied in the minerals industry, due to its greater efficiency in comparison with conventional tumbling mills. In this context, the agitator liner wear plays an important role in maintenance planning and operational costs. In this paper, we use the discrete element method (DEM) wear simulation to evaluate the screw liner wear. Three different mill rotational velocities are evaluated in the simulation, according to different scale-up procedures. The wear profile, wear measurement, power consumption, and particle contact information are used for obtaining a better understanding of the wear behavior and its effects on grinding mechanisms. Data from a vertical stirred mill screw liner wear measurement obtained in a full-scale mill are used to correlate with simulation results. The results indicate a relative agreement with industrial measurement in most of the liner lifecycle, when using a proper mill velocity scale-up.

Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data

The article presents an algorithm for fast and error-free determination of statistics such as the arithmetic mean and variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data. Additionally, it shows that both floating-point and integer representation can be used to perform this kind of statistical calculations. The author proves a theorem on the number of bits of precision that an arithmetic type must have to guarantee error-free determination of the arithmetic mean and variance. The article also presents the extension of Welford’s formula for determining variance for the sliding window method—determining the variance of fixed-length contiguous subsequences. The section dedicated to implementation tests shows the running times of individual algorithms depending on the arithmetic type used. The research shows that the use of integers in calculations makes the determination of the aforementioned statistics much faster.

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

Machine learning in industrial measurement technology for detection of known and unknown faults of equipment and sensors

This paper focuses on the application of novelty detection in combination with supervised fault classification for industrial condition monitoring. Its goal is to provide a guideline for engineers on how to apply novelty detection for outlier detection, monitoring of supervised classification and detection of unknown faults without the need of a data scientist. All guidelines are demonstrated by means of a publicly available condition monitoring dataset. In each application case the results achieved with different common novelty detection algorithms are compared, advantages and disadvantages of the respective algorithms are shown. To increase applicability of the suggested approach visualization of results is emphasized and all algorithms have been included in a publicly available data analysis software toolbox with graphical user interface.