Pawel Stefaniak

Procedures for Decision Thresholds Finding in Maintenance Management of Belt Conveyor System – Statistical Modeling of Diagnostic Data

Belt conveyors are a key component in material transportation system in both opencast lignite mining and underground copper mines in Poland. Regardless of the structure of the mine, the problem of maintenance of belt conveyors is important (from the entire mining process point of view) for many reasons, such as: (a) conveyors are spatially distributed over a large area, (b) they create logically structured form of complex and heavy components, (c) they are operating in harsh mining environmental conditions, (d) failure of any belt conveyor might result in downtime of the entire production line or its major part. The paper discusses the issue of maintenance of gearboxes used in the conveyor drive systems. The authors have developed a CMMS-class system using GIS technology to support management of conveyors’ network. Its fundamental role is to make right decisions for the exchange of components of the drive systems or allow them to continue their work. Such defined problem requires determination of complex decision rules and the definition of appropriate thresholds of diagnostic parameters. The article presents the procedures for determining decision thresholds, based on statistical modeling of diagnostic data and multidimensional data clustering. By selection of suitable distribution of the data and appropriate statistical parameters, multidimensional data analysis has been performed to determine threshold values for the effective identification of the condition of machines and their components.

Computerised Decision-Making Support System Based on Data Fusion for Machinery System’s Management and Maintenance

In the present fast-paced world of business in a highly competitive marketplace, the focus on increasing the efficiency of business processes seems to be a reasonable challenge. For this reason in order to optimize production costs, there is an increasing trend toward maximising the use of the operational capabilities of the equipment – technological line components – with the prevention of failure events and their consequences in the form of costly repairs or replacements. A condition-based maintenance (CBM) approach allows to globally monitor, maintain and control operation of the whole complex of equipment and/or processes. The CBM effectively supports decision-making process concerning their further use or determination of the optimum repair/replacement schedule. The presented decision support system is dedicated for an underground copper mine, where the network of belt conveyors is a critical part of the production process. Due to complexity of mechanical system, harsh mining environment and presence of many degradation factors, development of the effective CBM system seems to be justified. It requires the integration of data from different sources, adaptation of advanced data mining techniques, procedures or various diagnostic methods. 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 based on artificial intelligence techniques. Consequently, it allows to achieve improvement of efficiency of transportation network and reduction of repairs costs and unplanned breakdowns. In this paper we will briefly refer to all these issues.

Some Remarks on Using Condition Monitoring for Spatially Distributed Mechanical System Belt Conveyor Network in Underground Mine – A Case Study

The paper deals with application of condition monitoring and information system to maintain of complex, spatially distributed machinery system, namely belt conveyor transportation network, which consists of hundreds of drive units located on mine territory. There is simple question: what managers/engineers should do to ensure safe and efficient work of transportation machines? It has appeared that number of objects, their spatial location, specific structure of mining company, harsh environment, diversity of machines etc make this problem really complicated. It is obvious that there is a need to use specialized equipment, software but first of all set of procedures of data acquisition, validation, processing, storage, visualization, decision making, reporting etc, so in other words maintenance management. All these stages, combined and implemented as maintenance management software called Diag Manager (CMMS class) is discussed here.

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.

Multidimensional Signal Analysis for Technical Condition, Operation and Performance Understanding of Heavy Duty Mining Machines

Continuous improvement of production efficiency, safety and reliability of machines’ operation requires implementation of modern technology in the company, including monitoring systems, IT solutions, computer aided management tools etc. Gathering of data describing processes, extraction of information and knowledge discovery in automatic way seem to be key strategy in order to enhance company’s performance in many contexts. In this paper we will refer to the current status of the system being developed in one of the biggest Polish mining companies. A special attention will be paid to signal validation, pre-processing and analysis in order to retrieve unknown knowledge about machine condition, processes executed on a daily basis and machine/operator performance.

Novel Techniques of Diagnostic Data Processing for Belt Conveyor Maintenance

In the paper a new diagnostic approach for gearbox used in belt conveyors will be discussed. The purpose of the work is to provide novel view on diagnostic data processing in the context of detection of changes in condition for population of gearboxes used in belt conveyor network. The idea will be presented by examples: a data base of diagnostic features collected during last 3 years (real data from conveyors operating in mining company) will be used for illustration.

Diagnostic Features Modeling for Decision Boundaries Calculation for Maintenance of Gearboxes Used in Belt Conveyor System

Condition-Based maintenance (CBM) becomes more and more popular in industry. The idea is simple: measure raw data (vibrations, temperatures, etc.), extract features and make a right decisions regarding replacement of the whole machine or its component at appropriate time. Right decision might mean simple if-then-else rule or complex decision making scheme using multidimensional data. In any case mentioned rules require definition of appropriate thresholds for diagnostic parameters (i.e. decision boundaries). This is a key problem in CBM. The article presents the procedure for determining decision thresholds based on statistical modeling of diagnostic data. In the presented procedure first we fit the suitable distribution (Weibull) to data set for each gearbox. Next we calculate the fitting quality measure and select the distribution parameters for well fitted data. Finally, on the basis of the multidimensional analysis of those parameters we determine threshold values for the effective identification of the machines’ condition and their components. It might be interpreted as training process of diagnostic system. From this phase of the procedure we can obtain thresholds for warning and alarm statuses and they can be used for classification of rest of the data (that did not pass modeling phase). Proposed procedure has been applied to relatively large diagnostic data set that covers nearly 150 measurements acquired during several years in underground mine. The data describes gearboxes in different conditions—from nearly new or after repair to seriously damaged/worn just before failure.

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.

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.