Wayside Hot-box Research Articles

Defect detection in freight railcar tapered-roller bearings using vibration techniques

Currently, there are two types of defect detection systems used to monitor the health of freight railcar bearings in service: wayside hot-box detection systems and trackside acoustic detection systems. These systems have proven to be inefficient in accurately determining bearing health, especially in the early stages of defect development. To that end, a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center, Inc. in Pueblo, CO. The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective tapered-roller bearing components with defect areas smaller than 12.9 cm2 while in service.

A wayside hotbox system with fuzzy and fault detection algorithms in IIoT environment

Moving towards industrial internet of things (IIoT) concept is one of the hot issues of the management, control, and maintenance of railway transportation. The wayside hotbox systems are useful for monitoring of overheated axle bearings in the harsh dust environment. In the paper, the fuzzy-based fault detection algorithm (FDA) of the spatially distributed hotbox monitoring system (SDHMS) and its IIoT concept are presented. SDHMS consists of three stand-alone wayside hotbox monitoring systems that are installed in three distant locations: two thermal power plants and a coal mine. The system plays an important role in keeping railway safety, preventive maintenance and accident avoidance. Therefore, the IIoT concept is needed for increasing the reliability of coal transportation and the energy efficiency of electricity production. The main advantage of the presented concept is the application of the FDA at the edge and fog levels as well as the application of a complex fuzzy model for decision making at the cloud level. Edge computing involves signal processing in hotbox systems with the aim of the fault detection and quality assessment of the measured signals. Fog computing involves data processing with the aim of detecting an overheated bearing (temperature, position, axle number and train side), and alarming. Cloud computing is in charge of assessing the bearing condition and the need for replacement and maintenance planning. The proposed algorithm and fuzzy model were validated in real conditions of exploitation, successfully.

Toward the Future—Upgrading Existing Remote Monitoring Concepts to IIoT Concepts

The futuristic idea of smart factories should take into account existing industrial facilities and the practical application of IIoT in them. The greatest benefits from the implementation of IIoT concepts are expected in the modification and modernization of complex technical systems. The modification of existing concepts of multiparameter monitoring systems into IIoT concepts is one of the hot issues of IIoT. This article deals with the practical aspect of this issue and provides an overview of two procedures for transforming existing three monitoring system concepts into IIoT concepts. The first system consists of three wayside hotbox monitoring systems that are installed in three distant locations. This system plays an important role in keeping railway safety, preventive maintenance, and accident avoidance, and thus provides reliable transport and accumulation of coal in two thermal power plants. The second one is useful for monitoring the presence of the vessels in the lock gate zones and controlling water traffic through the river lock. The third system is useful for infrared temperature measuring and detecting of overheated water-cooled rotor poles of hydrogenerators in the hydropower plant. The existing concepts of three remote multiparameter monitoring systems, as well as their novel IIoT concepts and multilayered frameworks, are described in detail.

An investigation into wayside hot-box detector efficacy and optimization

ABSTRACT Wayside hot-box detectors (HBDs) are devices used to assess the health of railcar components including bearings, axles, and brakes by monitoring their temperatures. HBDs use infrared (IR) sensors to record the temperatures of railroad bearings. Bearings that trigger an alarm or exhibit warm trending are removed and sent for inspection. In many cases, no discernable defects were found in the flagged bearings. Motivated by this finding, an investigation was conducted which included performing a controlled field test as well as exhaustive laboratory testing utilizing an HBD simulator. Data acquired from field and laboratory testing was used to evaluate the accuracy and efficacy of wayside HBDs. The results suggest that the scanning location on the bearing cup significantly affects the temperature measurement. Different calibrations for the field- and laboratory-acquired data were also explored. An optimized calibration technique along with proper IR sensor alignment can markedly improve the accuracy of HBD measurements.

English

In the railroad industry, troubled bearings in service are primarily identified using wayside hot-box detectors (HBDs). Several bearings set-out for trending and classified as non-verified, due to no visible damage within a cone assembly. Subsequent laboratory experiments were performed to determine a minimum temperature and environment necessary to reproduce these discolorations which are mostly due to roller temperatures greater than 232 o C (450 o F) for periods of at least 4hours. Research is going on the possibility that rollers reaching such elevated temperatures without heating the bearing cup (outer race) to a temperature significant enough to trigger the HBDs. Considering the previous experimental and analytical work, a static-thermal finite element analysis (FEA) of a railroad bearing pressed onto an axle and was analyzed using ANSYS. Analysis is carried out by considering certain radial load and thrust load. Present work shows the stresses at various temperatures, fatigue life, vibration characteristics and the dynamic response of a structure under the action of timedependent loads. Also Present work evaluates the Hot Box Detector (HBD) temperature by showing the bearing safe temperature, failure temperature and stresses under cyclic loading.

Thermal Modeling of a Railroad Tapered-Roller Bearing Using Finite Element Analysis

In the railroad industry, distressed bearings in service are primarily identified using wayside hot-box detectors (HBDs). Current technology has expanded the role of these detectors to monitor bearings that appear to “warm trend” relative to the average temperatures of the remainder of bearings on the train. Several bearings set-out for trending and classified as nonverified, meaning no discernible damage, revealed that a common feature was discoloration of rollers within a cone (inner race) assembly. Subsequent laboratory experiments were performed to determine a minimum temperature and environment necessary to reproduce these discolorations and concluded that the discoloration is most likely due to roller temperatures greater than 232 °C (450 °F) for periods of at least 4 h. The latter finding sparked several discussions and speculations in the railroad industry as to whether it is possible to have rollers reaching such elevated temperatures without heating the bearing cup (outer race) to a temperature significant enough to trigger the HBDs. With this motivation, and based on previous experimental and analytical work, a thermal finite element analysis (FEA) of a railroad bearing pressed onto an axle was conducted using ALGOR 20.3™. The finite element (FE) model was used to simulate different heating scenarios with the purpose of obtaining the temperatures of internal components of the bearing assembly, as well as the heat generation rates and the bearing cup surface temperature. The results showed that, even though some rollers can reach unsafe operating temperatures, the bearing cup surface temperature does not exhibit levels that would trigger HBD alarms.

The Fundamentals of Infrared Hotbox Detection

Existing data on railroad car plain bearing temperatures are presented and interpreted by means of equivalent thermal circuits. Analyzing the radiation from the car truck, relating it to the characteristics of infrared measuring equipment, and discussing the handling of resulting data-all suggest approaches to more efficient use of wayside hotbox detectors. Effects of some ambient phenomena are presented, and further study of transient temperature conditions following lubrication failure is suggested.