Automatic detection of sensor calibration errors in mining industry

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Sensor errors cost the mining industry millions of dollars in losses each year. Unlike gross errors, "calibration errors" are subtle, develop over time, and are difficult to identify. Economic losses start accumulating even when errors are small. Therefore, the aim of this research was to develop methods to identify calibration errors well before they become obvious. The goal in this research was to detect errors at a bias as low as 2% in magnitude. The innovative strategy developed relied on relationships between a variety of sensors to detect when a given sensor started to stray. Sensors in a carbon stripping circuit at a gold processing facility (Pogo Mine) in Alaska were chosen for the study. The results from the initial application of classical statistical methods like correlation, aggregation and principal component analysis (PCA), and the signal processing methods (FFT), to find bias (±10%) in "feed" sensor data from a semi-autogenous (SAG) grinding mill operation (Fort Knox mine, Alaska) were not promising due to the non-linear and non-stationary nature of the process characteristics. Therefore, those techniques were replaced with some innovative data mining techniques when the focus shifted to Pogo Mine, where the task was to detect calibration errors in strip vessel temperature sensors in the carbon stripping circuit. The new techniques used data from two strip vessel temperature sensors (S1 and S2), four heat exchanger related temperature sensors (H1 through H4), barren flow sensor (BARNFL) and a glycol flow sensor (GLYFL). These eight sensors were deemed to be part of the same process. To detect when the calibration of one of the strip vessel temperature sensors, S1, started to stray, tests were designed to detect changes in relationship between the eight temperature sensors. Data was filtered ("threshold") based on process characteristics prior to being used in tests. The tests combined basic concepts such as moving windows of time, ratios (ratio of one sensor data to data from a set of sensors), tracking of maximum values, etc. Error was triggered when certain rules were violated. A 2% error was randomly introduced into one of the two strip vessel temperature data streams to simulate calibration errors. Some tests were less effective than others at detecting the simulated errors. The tests that used GLYFL and BARNFL were not very effective. On the other hand, the tests that used total "Heat" of all the heat exchanger sensors were very effective. When the tests were administered together ("Combined test"), they have a high success rate (95%) in terms of True alarms, i.e., tests detecting bias after it is introduced. In those True alarms, for 75% of the cases, the introduction of the error was detected within 39.5 days. A -2% random error was detected with a similar success rate

The Importance of Specific Phrases in Automatically Classifying Mine Accident Narratives Using Natural Language Processing

The mining industry is diligent about reporting on safety incidents. However, these reports are not necessarily analyzed holistically to gain deep insights. Previously, it was demonstrated that mine accident narratives at a partner mine site could be automatically classified using natural language processing (NLP)-based random forest (RF) models developed, using narratives from the United States Mine Safety and Health Administration (MSHA) database. Classification of narratives is important from a holistic perspective as it affects safety intervention strategies. This paper continued the work to improve the RF classification performance in the category “caught in”. In this context, three approaches were presented in the paper. At first, two new methods were developed, named, the similarity score (SS) method and the accident-specific expert choice vocabulary (ASECV) method. The SS method focused on words or phrases that occurred most frequently, while the ASECV, a heuristic approach, focused on a narrow set of phrases. The two methods were tested with a series of experiments (iterations) on the MSHA narratives of accident category “caught in”. The SS method was not very successful due to its high false positive rates. The ASECV method, on the other hand, had low false positive rates. As a third approach (the “stacking” method), when a highly successful incidence (iteration) from ASECV method was applied in combination with the previously developed RF model (by stacking), the overall predictability of the combined model improved from 71% to 73.28%. Thus, the research showed that some phrases are key to describing particular (“caught in” in this case) types of accidents

Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine

To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data