Jacek Wodecki

Self-propelled Mining Machine Monitoring System – Data Validation, Processing and Analysis

Self-propelled Mining Machines constitute large group of basic machines in underground copper ore mining in Poland. Depends on their purpose and design there are several key parameters that (according to mining companies suggestions) should be monitored and processed in order to assess machine efficiency, its condition, proper operation (according to manufacturer recommendation), human factors influence and so on. Several studies have been done regarding selection of parameters, developing algorithms of data processing, data storage and management and finally reporting and visualization of knowledge extracted from measured data. Although serious efforts have been done in this field, there is still some work to do. In this paper, a new look on the problem will be presented including data acquisition process validation, importance of data quality for automatic processing and analysis. Finally new approach for signal analysis will be proposed and compared with already existing parameters. Also kind of target re-definition attempt will be discussed. All discussed issues will be illustrated using real data acquired during machine operation.

Technical condition change detection using Anderson–Darling statistic approach for LHD machines – engine overheating problem

Abstract In underground mine so called Load-Haul-Dump machines (LHD) plays a key role in horizontal transportation process. LHD machines execute ore haulage from mining faces to dumping points in a cyclic way. Time-varying and harsh environmental conditions determine high workload, so effectiveness demands are big challenges for maintenance staff. One of the most important issue is related to engine overheating, what is the main cause of unjustifiable loader stoppages and unwanted disturbances in production. Operator is obligated to react quickly and switch machine to idle operation until it cools down. Existing on-board monitoring systems dedicated for LHD machines provide data necessary to perform diagnostics of the engine as well as its cooling system. Understanding how load, wear level of machine and ambient temperature influence diagnostic data is the key in development of fault detection algorithms. In this paper, authors propose to use longterm temperature data. The Anderson–Darling statistic is applied in order to detect segments of different statistical properties which are related to different technical condition. Analysis of obtained two-dimensional data structure allows to find points of change of technical condition of the machine. It could be considered as training for diagnostic system that could be used for machine monitoring.

Maintenance Management of Mining Belt Conveyor System Based on Data Fusion and Advanced Analytics

Belt conveyor network is an important transportation form used in the underground copper ore mines. Effective maintenance of this infrastructure is critical–serious failure of single conveyor might stop operation of several conveyors connected in series and finally might affect production volume. To achieve expected reliability, one should use appropriate tools for supporting maintenance management and decision-making process. Nowadays, predictive maintenance seems to be the most powerful approach for industrial applications. Deep understanding design and operational factors, knowledge about of repairs (number, type, reasons, etc.), and finally acquisition and processing of appropriate physical variables might provide suitable information for maintenance staff. However, mining industry, especially underground mine is a specific kind of factory. Harsh environmental conditions (high humidity, temperature, dust, etc.), varying load, specific damage scenarios make practical implementation of predictive maintenance difficult. Also a scale of transportation system plays an important role: >80 conveyors with different configurations (1–4 drives), diversity of dimensions (short . long conveyors), locations (environmental issues, operation on the slope), etc. All these facts required special approach based on diagnostic data acquisition, necessary processing and context-based reasoning. In this paper, we will discuss details related to development of analytical IT-based environment integrating data from many different sources and procedures supporting decision-making process. We will propose the concept of Decision Support System for maintenance of conveyors system. Because of the multidimensional nature of diagnostic data and diversified technical configurations of the facilities, it was necessary to develop and implement multivariate analytical models including data fusion and artificial intelligence techniques. Consequently, it allows to avoid failures, supports scheduling repairs, and finally provides reduction of repairs costs and production losses related to breakdowns.

Multidimensional Data Segmentation Based on Blind Source Separation and Statistical Analysis

Horizontal transport in underground copper ore mines mainly consists of LHD machines (loaders, haulers ) and belt conveyors. One of the most crucial mining issues for assessment of efficiency of production is identification of operation cycles of haulage machines. In the literature one can find procedure based on analyzing of pressure signal variability developed for loader (Polak et al Identification of loading process based on hydraulic pressure signal pp 459–466, 2016, Stefaniak et al An effectiveness indicator for a mining loader based on the pressure signal measured at a bucket’s hydraulic cylinder 15, pp 797–805 [6, 7]). The algorithm allows to identify partial operations of loader cycles like: loading, haulage and return to mining face. For haulers this task can seem to be very easy to solve—machines are driving from point A to point B. Nevertheless, when we take into account harsh and specific conditions of underground mine, the problem remains very hard to solve using classical methods based on single variable and if-then-else rules. In most cases, those methods are not robust enough due many random factors (logistical, human factors, work organisation with loaders etc.). In this paper, we propose some kind of data fusion approach to recognition of partial hauler operations. Our method is based on blind source separation approach with particular focus on independent component analysis technique that uses JADE algorithm based on joint approximate diagonalization of eigenmatrices. Obtained components allow for easy segmentation of the signals.

Unsupervised Anomaly Detection for Conveyor Temperature SCADA Data

Belt conveyor system is a crucial element of ore transport process in underground copper ore mine. Damage of single belt conveyor might cause stopping of huge part of underground transport network, especially when failure concerns the main haulage conveyor line. For that reason it is important to use SCADA monitoring system. The symptom of damage can be found in increasing temperature measured within the system. Unfortunately, operating belt conveyors can be considered as time-varying system and direct decision making using temperature value is difficult. Long-term analysis of time series enables to learn how to recognize alarming moment. Thus the removal of failure can be scheduled so as to minimize the losses in production. In this paper the clustering method was applied to the long-term observations of the temperature in order to gearbox fault detection. Moreover, the breaks in the activity of belt conveyors (no operation) caused by holidays will be determined. The clustering algorithm identifies also the specific character of the work at the beginning and end of week.

Application of Independent Component Analysis in Temperature Data Analysis for Gearbox Fault Detection

In the real multisource signal analysis one of the main problems is the fact that true information is divided partially among the individual signals and/or measured signal is a mixture of different sources. This comes from the fact that input channels are typically related, and carry information about different processes occurring during the measurement. Those processes can be thought of as independent sources of vaguely understood “information”. In many cases separation and extraction of those sources can be crucial. In this paper we present the usage of Independent Component Analysis as a tool for information extraction from real-life multichannel temperature data measured on heavy duty gearboxes used in mining industry. Original signals, due to operational factors reveal cyclic variability and detection of damage was difficult. Thanks to proposed procedure, from four channels acquired from 4 gearboxes driving belt conveyor we have extracted one of 4 components, that is related to change of condition of a single gearbox. For new signal visibility of change is clear and simple automatic detection rule can be applied.

Novel method of informative frequency band selection for vibration signal using nonnegative matrix factorization of short-time fourier transform

The problem of local damage detection in rotating machines is currently the highly important subject of interest. In the literature one can find many different strategies. One of the most common approaches is the vibration signal analysis aiming at informative frequency band selection. In case of simply structured signals classic methods (e.g. spectral kurtosis) are sufficient and return clear information about the damage. However, in real-world cases the signal is usually much more complicated. Indeed, such signals consist of many different components, for instance: damage-related cyclic impulses, high energy non-cyclic impulses not related to damage or heavy-tailed background noise etc. Hence, there is a growing need for robust damage detection methods. In this paper a novel method of informative frequency band selection is proposed. It utilizes the approach of Non-negative Matrix Factorization applied to time-frequency signal representation. The described algorithm is evaluated using simulated signal containing several different components, that resembles real-life vibration signal from copper ore crusher. Using the obtained structure of informative frequency band it is possible to filter particular components out of the original signal.