Discrete Fourier transform and cepstrum analysis of vibration events on semi-trailer truck

Monitoring the condition of rotating machines is essential for the systems' safety, reducing maintenance costs, and increasing reliability. In this research, a fault detection system for bearings was developed using the vibration analysis technique with the statistical control chart approach. A test rig was first designed and constructed; then, various bearing faults, such as inner race and outer race faults, were simulated and examined in the test rig. After capturing the vibration signals at different bearing health conditions, the time-domain signal analysis technique was employed for extracting different indicative features. The obtained time domain features were then analyzed to find out the most fault-significant feature. Then, only one feature was selected to design the control chart for bearing health condition monitoring. The cumulative sum control chart (CUSUM was utilized since it can detect the small changes in bearing health states. The results showed the effectiveness of utilizing this method, and it was found that the percentage of the out-of-control points in the event of the combined cage and ball fault to the number of tested samples is greater than the other fault types.

Section: Frequency Domain Techniquesmentioning

confidence: 99%

Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart

Mohammed¹,

Abdulhady²

2021

“…Only two studies observed shock events during truck transportation [13][14]. In the case of vibration analysis, power density (PD) levels are determined as a function of frequency based on recorded random vibration acceleration levels [15][16][17][18]. But shock and vibration events must be separately examined to conduct packaging laboratory simulations [19].…”

Section: Introductionmentioning

confidence: 99%

Impact shock events in multimodal container transshipment for packaging testing

Németh¹,

Böröcz²

2023

In modern global supply chains, intermodal and multimodal distribution has become essential means of transportation. The combination of different modes of transport is the most commonly used method for distributing shipments between continents. This paper examines and measures physical events, such as shock and impact, that occur while transporting 40ft long ISO containers using multiple modes of transport. These events can directly affect the integrity of packaged products and cause damage. The study focuses on events such as transshipments and handling of containers in hubs and terminals. The impact shock levels were separately analyzed in all three-dimensional directions, namely vertical, longitudinal, and lateral. The results indicate the percentage of occurrence below a given impact level using statistical characteristics of the events that occurred. The magnitude and mean of acceleration levels, pulse duration, and velocity change are also reported.

Ai-enhanced fault diagnosis in rolling element bearings: A comprehensive vibration analysis approach

Samal,

Sunil,

Jamadar

et al. 2024

This research presents a comprehensive approach for bearing fault diagnosis using artificial intelligence (AI), particularly through the application of artificial neural networks (ANNs). By integrating these networks into vibration analysis, the approach aims to meet the critical need for prompt fault detection. The methodology comprises three key steps: vibration signal acquisition, feature extraction, and fault classification. Experiments were conducted to acquire vibration signals for the test bearings on a machinery fault simulator. Six time-domain features were extracted using MATLAB, creating a comprehensive dataset for training the ANN models with three algorithms: Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGBP), and Bayesian regularization backpropagation (BRBP). The BRBP algorithm achieved the highest correct classification rate (97.2%), followed by LMBP (90%) and SCGBP (83.6%). To evaluate their efficacy in bearing fault classification, these three networks were simulated, revealing that BRBP could predict all four classes of bearings with zero errors.