Data-Driven Condition Monitoring of Mining Mobile Machinery in Non-Stationary Operations Using Wireless Accelerometer Sensor Modules

Abstract

This paper presents the development of an easy-to-deploy and smart monitoring IoT system that utilizes vibration measurement devices to assess real-time condition of bulldozers, power shovels and backhoes, in non-stationary operations in the mining industry. According to operating experience data and the type of mining machine, total loss failure rates per machine fleet can reach up to 30%. Vibration analysis techniques are commonly used for condition monitoring and early detection of unforeseen failures to generate predictive maintenance plans for heavy machinery. However, this maintenance strategy is intensively used only for stationary machines and/or mobile machinery in stationary operations. Today, there is a lack of proper solutions to detect and prevent critical failures for non-stationary machinery. This paper shows a cost-effective solution proposal for implementing a vibration sensor network with wireless communication and machine learning data-driven capabilities for condition monitoring of non-stationary heavy machinery in mining operations. During the machine operation, 3-axis accelerations were measured using two sensors deployed across the machine. The machine accelerations (amplitudes and frequencies) are measured in two different frequency spectrums to improve each sensing location's time resolution. Multiple machine learning algorithms use this machine data to assess conditions according to manufacturer recommendations and operational benchmarks Proposed data-driven machine learning models classify the machine condition in states according to the ISO 2372 standards for vibration severity: Good, Acceptable, Unsatisfactory, or Unacceptable. After performing field tests with bulldozers and backhoes from different manufacturers, the machine learning algorithms are able to classify machine health status with an accuracy between 85% -95%. Moreover, the system allows early detection of ''Unacceptable'' states between 120 to 170 hours prior to critical failure. These results demonstrate that the proposed system will collect relevant data to generate predictive maintenance plans and avoid unplanned downtimes.

Data mining in predictive maintenance systems: A taxonomy and systematic review

“…All these aspects make sensors widely used devices that allow connectivity by minimally altering an existing industrial environment, thus obtaining the monitoring required to apply PdM. Different kind of sensors are reported in last years in PdM for measuring vibration frequency (Bouabdallaoui et al, 2021; Chen et al, 2019; Cho et al, 2020; Liang et al, 2020; Oluwasegun & Jung, 2020; Orrù et al, 2020; Wang, Liu, et al, 2020; Zhou et al, 2019) or vibration acceleration alone (Aqueveque et al, 2021; Casoli et al, 2019; Malawade et al, 2021; Shamayleh et al, 2020; Yang et al, 2021) or in conjunction with relative position (Mishra & Huhtala, 2019). Other works use sensors that capture atmosphere‐based features like temperature (Axenie et al, 2020).…”

“…The most commonly used methods belong to the Fourier transform family which is applied to decompose the signal into components of different frequencies. Specifically, several works use the Fast Fourier Transformation (FFT) in order to reduce the feature space (Aqueveque et al, 2021; Casoli et al, 2019; Sampaio et al, 2019; Shamayleh et al, 2020; Zschech et al, 2019). The study of Song et al (2021) employed the Short‐time Fourier Transform, which focuses on determining the sinusoidal frequency and phase content of local sections of the signal as it changes over time.…”

“…Linear and RBF kernels in binary classification (Aremu, Hyland‐Wood, & McAree, 2020), and multiple failures (Casoli et al, 2019; Leahy et al, 2018) scenarios. In Aqueveque et al (2021), linear, RBF, and polynomial SVMs are compared, together with other methods, in the multiclass classification to predict health statuses. The comparisons report the best performance for RBF in all cases.…”

Development of a Cyber-Physical System to Monitor Early Failures Detection in Vibrating Screens

“…Figure 17 shows the framework of the active semi-supervised SVM machine learning algorithms used in this work to detect anomalies and predict progressive damage. The framework also was applied to implementing alternative predictive models based on Naïve Bayes algorithms with same purposes [48].…”

Design and Performance Evaluation of an Ultralow-Power Smart IoT Device With Embedded TinyML for Asset Activity Monitoring

“…This approach increases the energy required by the communication and the latency of the detection and additionally poses security and privacy risks. For this reason, today's sensor systems mostly focus on monitoring medium to large-sized stationary machinery or tools [24]. On the other side, designing a truly intelligent IoT node for such tools, brings challenges on both the size and the life-cycle, mainly due to limited batteries that supply the smart sensor node [25].…”