Automatic calculation of thresholds for load dependent condition indicators by modelling of probability distribution functions – maintenance of gearboxes used in mining conveying system

Limit values for gearbox vibration-based condition indicators are key to determine in order to be able to estimate moment when object is in a need of maintenance. Further decision making process usually might utilize simple if-then-else rule using established threshold values. If diagnostic data takes the values from the Gaussian distribution, finding the decision boundaries is not difficult. Simplistically, that comes down to standard pattern recognition technique for “good condition” and “bad condition” based on probability density functions (PDFs) of diagnostic data. This situation is becoming more and more complicated when distribution is not Gaussian. Such cases require to develop much more advanced analytically solution. In this paper, we present the case of belt conveyor’s gearbox for which PDFs of diagnostic features overlap each other because of strong influence of time varying operating conditions on spectral features. New approach to automatic threshold recognition has been proposed based on modeling diagnostic features with Weibull distribution and using agglomerative clustering to distinguish classes of technical condition, which leads to determination of thresholds separating them

Combination of ICA and time-frequency representations of multichannel vibration data for gearbox fault detection

In the paper a multichannel vibration data processing method is presented in the context of local damage detection in gearboxes. The purpose of the approach is to obtain more reliable information about local damage when using several channels in comparison to results obtained for single channel vibration. The method is a combination of time-frequency representation and Independent Component Analysis (ICA) but applied not to raw time series but to each slice (along to time) from spectrogram. Finally we create new time-frequency map, that after aggregation clearly indicates presence of damage. In the paper we will present details of the method and benefits of using our procedure. We will refer to autocorrelation function of mentioned aggregated new time frequency map (1D signal) or simple spectrum (that might be somehow linked to classical envelope analysis). We believe that results are very convincing – detection of cyclic impulses associated to local damage are clearly identifiable. To validate our method we use real vibration data from heavy duty gearbox used in mining industry

Segmentation algorithm of roadheader vibration signal based on the stable distribution parameters

In the real signal analysis the main problem is the non-stationarity of given data. The non-stationarity can be manifested in different ways. One of the possibility is the assumption that the signal is a mixture of different processes that exhibit different statistical properties. Thus before the further analysis the observed data should be segmented. In this paper we propose an automatic segmentation method which is based on α-stable distribution approach. In the proposed procedure we estimate the parameters of stable distribution for consecutive sub-signals of given length and then by using expectation-maximization algorithm we classify the parameters. The obtained classes correspond to different segments of the signal. The proposed procedure we apply to the real vibration signal from roadheader working in mining industry. As a final result we obtained segments of real signal which constitute samples of different behaviors and are related to different modes of operation of the machine

Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection

A multichannel vibration data processing method in the context of local damage detection in gearboxes is presented in this paper. The purpose of the approach is to achieve more reliable information about local damage by using several channels in comparison to results obtained by single channel vibration analysis. The method is a combination of time-frequency representation and Principal Component Analysis (PCA) applied not to the raw time series but to each slice (along the time) from its spectrogram. Finally, we create a new time-frequency map which aggregated clearly indicates presence of the damage. Details and properties of this procedure are described in this paper, along with comparison to single-channel results. We refer to autocorrelation function of the new aggregated time frequency map (1D signal) or simple spectrum (that might be somehow linked to classical envelope analysis). The results are very convincing – cyclic impulses associated with local damage might be clearly detected. In order to validate our method, we used a model of vibration data from heavy duty gearbox exploited in mining industry

SEC4TD Project To Improve the Safety of Tailings Storage Facilities

Tailings storage facilities (TSFs) are structures designed to contain tailings (a byproduct of extracting valuable minerals and metals from mined ore) and to manage associated water. Despite all the data collected and a basic understanding of the mechanisms resulting in tailings dam failures, these large structures have consistently failed over the past 50 years, causing human and economic losses and huge environmental consequences to ecosystems and local communities. Therefore, the day-to-day management of these structures is a very challenging task. One needs to focus not only on keeping the tailings discharge plan but also on the construction (the constantly raised embankments) and the structural safety of the TSFs. The operational controls comprise inspections, surveys, installation of the monitoring instrumentations, interpretation of the monitoring readings, and safety analysis of the structure. The article presents the SEC4TD project as a tool to assist the engineering and management staff in day-to-day operations related to keeping the safety of the facility structure

The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Żelazny Most Facility

<p>Approximately 30 million tons of tailings are being stored each year at the KGHMs Zelazny Most Tailings Storage Facility (TSF). Covering an area of almost 1.6 thousand hectares, and being surrounded by dams of a total length of 14 km and height of over 70 m in some areas, makes it the largest reservoir of post-flotation tailings in Europe and the second-largest in the world. With approximately 2900 monitoring instruments and measuring points surrounding the facility, Zelazny Most is a subject of round-the-clock monitoring, which for safety and economic reasons is crucial not only for the immediate surroundings of the facility but for the entire region. The monitoring network can be divided into four main groups: (a) geotechnical, consisting mostly of inclinometers and VW pore pressure transducers, (b) hydrological with piezometers and water level gauges, (c) geodetic survey with laser and GPS measurements, as well as surface and in-depth benchmarks, (d) seismic network, consisting primarily of accelerometer stations. Separately a variety of different chemical analyses are conducted, in parallel with spigotting processes and relief wells monitorin. This leads to a large amount of data that is difficult to analyze with conventional methods. In this article, we discuss a machine learning-driven approach which should improve the quality of the monitoring and maintenance of such facilities. Overview of the main algorithms developed to determine the stability parameters or classification of tailings are presented. The concepts described in this article will be further developed in the IlluMINEation project (H2020).</p&gt

Methods of Optimization of Mining Operations in a Deep Mine—Tracking the Dynamic Overloads Using IoT Sensor

Self-propelled machines are the main resources used by the Polish copper ore mining industry to transport ore from the mining area to reloading points for conveyor transport. Due to the difficult mining conditions, they must meet high requirements in terms of operational efficiency, safety, and reliability. One of the most significant challenges is high robustness to dynamic overloads. In practice, they have a strong dependency on pavement influence during machine movement, the type of operation, and the driving style of the operator. In this research, we focus on the multivariate analysis of dynamic overloads observed on a large population of haul trucks operating in different mining areas. The main aim of this study was the identification of major factors of excessive dynamic overload that result in damage to structural nodes of machines. In the case of haul track, the joint is such a critical component, that in extreme situations, it breaks and splits the machine in two. There are proposed methods for assessing the occurrence of dynamic overload based on recognized mining conditions and operator behavior. In addition, we propose a method to specify which factors are more meaningful for dynamic overloads. A measurement campaign has been conducted using a mobile inertial sensor interconnected with a developing IoT platform for predictive maintenance of mining infrastructure