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

Stefaniak, Paweł; Zimroz, Radosław; Śliwiński, Paweł; Andrzejewski, Marek; Wyłomańska, Agnieszka

doi:10.1007/978-3-319-20463-5_15

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…In many actual cases, the operating conditions (rotational speed, loads, temperatures) vary during the lifetime of a bearing (27), e.g., for bearings used in trains, cars (28), mining machines (29) or wind turbines (30)(31)(32). In these cases, it is almost impossible to obtain several baseline signals (such as those considered in (20)) for every operating condition in real time.…”

Section: Fig 2 Sequence Of Records During the Lifetime Of A Bearingmentioning

confidence: 99%

A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density

Pennacchi

Chatterton

2020

Mechanical Systems and Signal Processing

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In the broad framework of condition-based maintenance, the final objective of bearing condition monitoring is to evaluate the health state and to estimate the remaining useful life of the bearings. The latter is a particularly challenging task, considering that remaining useful life is inextricably linked to a projection of what will happen in the future. Often, health indices, whose reliability relies on their effectiveness and consistency, are used for bearing condition monitoring. Most of the existing health indices pursue the property of monotonicity, but generally there are no obvious boundaries between the different health states of the bearings. Hence, it is quite difficult to give an objective and independent estimation of the health states, especially in real applications under different operating conditions and in the presence of noise. Furthermore, it is also difficult to set the failure threshold for a given health index when it is employed in different applications. In this paper, a new health index called 'MAC 2 PSD' is proposed, based on the moving-average cross-correlation (MAC 2) of the power spectral density (PSD) of the vibration signals. An interesting property of MAC 2 PSD is its capability to track the health condition and to discriminate clearly between the different health states. As shown in the paper, the MAC 2 PSD can also be used to estimate the remaining useful life by using its values during the defect-propagation phase. The effectiveness of MAC 2 PSD is shown by means of two different cases of bearing run-to-failure experimental data, from two different test rigs. Additionally, the capability to avoid false positives is evaluated by means of bearing vibration data measured on a locomotive in commercial service.

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Section: Fig 2 Sequence Of Records During the Lifetime Of A Bearingmentioning

confidence: 99%

A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density

Pennacchi

Chatterton

2020

Mechanical Systems and Signal Processing

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“…Cycle extraction from process data has been discussed in [25]. Stefaniak et al proposed several algorithms for the multidimensional analysis of the data from an on-board monitoring system used in the underground heavy duty load-haul-dump trucks (LHDs) [26]. The problem of drilling robotization has been discussed in [27].…”

Section: Monitoring Of the Drilling Process-the State Of The Artmentioning

confidence: 99%

Process Monitoring in Heavy Duty Drilling Rigs—Data Acquisition System and Cycle Identification Algorithms

et al. 2020

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The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manufacturer by comparing the results with the data of the on-board monitoring system installed on the machine. Novel aspects of our approach include the detection of the pre-boring stage, which has an intermediate amplitude that masks the real drilling cycles, and the use of the percussion instantaneous frequency, which is estimated by acoustic recordings.

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“…For example, the machine will enter a less ventilated area of the mine, which will result in exceeding the temperature data thresholds not related to its technical condition. In addition, having location methods tailored to the specific conditions of an underground mine, it is possible to develop methods of machine navigation, production optimization (e.g., indicating optimal haulage routes, mitigation of blockages), development of anti-collision systems, crisis management (indicating evacuation routes), or autonomous machines [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm

et al. 2021

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This article presents the concept of using the DTW algorithm to partially solve the problem of locating LHD (load, haul, dump) in an underground mine. The concept assumes the recognition of characteristics—patterns that are hidden in vibrations recorded by vehicles—in segments of the route in the underground excavation, which under appropriate conditions enables the obtainment of information similar to that obtained through the use of RFID gates. With the use of this solution in practice, there are several problems that are addressed in this article. One of the main issues is the different arrangement of the signal fragments resulting from driving along with characteristic parts of the route (bumps, paving damage, lumps of excavated material, etc.) at different driving speeds. This problem was solved by using a combination of the road quality detection algorithm and the DTW algorithm, which estimates the similarity of time series with different lengths. The concept was developed and pre-tested using a test rig and a constructed wheeled robot, and then validated in the conditions of the KGHM underground copper mine in Poland, where the readings from the typical haulage process of an LHD vehicle were analyzed.

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

Cited by 14 publications

References 8 publications

A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density

A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density

Process Monitoring in Heavy Duty Drilling Rigs—Data Acquisition System and Cycle Identification Algorithms

Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm

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