Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures

In this study, we introduced an expert system (ESvbrPAL2v), responsible for monitoring assets based on vibration signature analysis through a set of algorithms based on the Paraconsistent Annotated Logic – PAL. Being a non-classical logic, the main feature of the PAL is to support contradictory inputs in its foundation. It is therefore suitable for building algorithmic models capable of performing out appropriate treatment for complex signals, such as those coming from vibration. The ESvbrPAL2v was built on an ATMega2560 microcontroller, where vibration signals were captured from the mechanical structures of the machines by sensors and, after receiving special treatment through the Discrete Fourier Transform (DFT), then properly modeled to paraconsistent logic signals and vibration patterns. Using the PAL fundamentals, vibration signature patterns were built for possible and known vibration issues stored in ESvbrPAL2v and continuously compared through configurations composed by a network of paraconsistent algorithms that detects anomalies and generate signals that will report on the current risk status of the machine in real time. The tests to confirm the efficiency of ESvbrPAL2v were performed in analyses initially carried out on small prototypes and, after the initial adjustments, tests were carried out on bearings of a group of medium-power motor generators built specifically for this study. The results are shown at the end of this study and have a high index of signature identification and risk of failure detection. These results justifies the method used and future applications considering that ESvbrPAL2v is still in its first version.

Experimental Study of the Effect of Misalignment on Rolling Element Bearing

Any single machine rotary component in the process could result in downtime costs. It is necessary to monitor the overall machine health while it is in use. Bearing failure is one of the primary causes of machine breakdown in industry at high and low speeds. A vibration signature evaluation has historically determined misalignments in shafting systems. These misalignments are also responsible for the bearing increase in temperature. The purpose of this work is to undertake a comparative study to obtain the reliability of the effect of the amount of misalignment on bearing by using thermography measurement. An experimental study was performed in this paper to indicate the existence of machine misalignment at an early stage by measuring the bearing temperature using a thermal imaging camera. The effects of load, velocity, and misalignment on the bearings and their temperature increase have been investigated. The test bench's rolling-element bearing is an NTN UCP213-208 pillow block bearing. It has been observed that by tracking the change of temperature in bearings could lead to misalignment detection and the effect of the amount of misalignment on it.

Prognostics and Availability for Industrial Equipment Using High Performance Computing (HPC) and AI Technology

The Industrial Internet of things (IIoT) enabled smart system has entered into a golden era of rapid technology growth. IIoT is a concept to make every system interrelated such that they are able to collect and transfer data over a wireless network without human intervention. In this paper, we discuss the development of an IoT enabled system to monitor the vibration signature of equipment as part of prognosis and availability management system (P&AM) that serves to prevent unplanned operation downtime and catastrophic failure of a whole system. In order to simply the complexity of processing video content and performing inference, the Intel OpenVINO platform was selected because of it’s simplicity, portability across Intel AI processors, performance and comprehensiveness of it’s analytical and diagnostics capabilities that can be tested in Intel’s DevCloud. The IIoT system consists of a High Performance Computing (HPC) platform based on Intel’s Xeon processors and Movidius AI accelerator, Intel’s OpenVINO toolkit for AI, a Regul high performance programmable controller capturing vibration data through sensors and a low-latency network connection. Notifications of anomalies are sent to a smartphone. This paper reveals an approach for the features extraction and selection, known as feature engineering, of the equipment component we want to protect. Feature engineering is the first step for the P&AM of these components and extends to the whole system. The broader aim of this paper is to help technical leaders at the exploring or experimenting stages of their AI framework to learn the concepts of implementing algorithms using datasets that have real value to their companies. Datasets generated and referred to in this paper were generated by simulation under various material failure scenarios.

Industrial fan vibration signature characterization

Technique for measuring and reducing industrial fan vibration and noise is detailed. A method used to characterize the vibration signature for 100% industrial fan systems shipped is described. A fan system consists of motor, propeller and cage. We measure triax accelerometer vibration, microphone (both sound pressure level in dBA and raw signal in Pa) along with the current of three phase power for each fan shipped. Comparisons are done immediately with the ISO 14694:2003 standard and troubleshooting and design changes are implemented if vibration limits are exceeded. The method and results are provided for several cases. Troubleshooting and best practices are described for various designs. A portable system takes measurements in the field which are compared to the factory baseline characterization in real time to solve installation problems.

An Automated Edge Computing-Based Condition Health Monitoring System: With an Application on Rolling Element Bearings

A failure of rolling element bearings is a frequent cause of machine breakdowns and results in a production loss due to the sudden failure. A regular condition health monitoring and an associated detection of bearing defects in the early stages can be used to predict such sudden failures. To monitor the bearing's condition, the generated vibration signature can be analyzed, since rotating machines have, in most instances, a unique vibration signature that relates to their health status. Presently, bearing analysis of many machines results in significant cost and complexity due to a large amount of vibration data that must be analyzed. A condition health monitoring system (CMS) was developed to automate and simplify the whole process from the vibration measurement to the analysis results. Additionally, the CMS is embedded into an Internet of Things (IoT) architecture. Thereby, a location-independent control of the CMS, the vibration data, and the analysis results is possible. The embedding of sensors can cause communication problems from the sensor to the cloud due to the low bandwidth of sensors and the amount of data that must be transmitted. To overcome this issue, an edge device that acts as a gateway between the vibration sensor and the cloud is the core of the CMS. It measures the vibration signal locally, analyzes it automatically, and publishes a feedback as to the bearing condition to the cloud.

Application of the Reassigned Smoothed Pseudo Wigner-Ville Distribution in Gear Defect Diagnosis

Gear is one of the most omnipresent components in the mechanical and industrial fields. Some defects can occur causing a change in the vibration signature. In the dynamic of gears system, several sources of excitation are considered. The principal objective of the present work is to study the use of the Reassigned Smoothed Pseudo Wigner Ville Distribution (RSPWVD) in the gear vibration monitoring. The used signals are obtained by the simulation of an eight degrees of freedom gearbox system for the healthy and cracked case. The considered model takes into account the presence of friction force inter teeth in contact, backlash and time varying mesh stiffness. Also, the presence of noise is highlighted. A comparison with the Smoothed Pseudo Wigner Ville Distribution (SPWVD) is done.