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.

Investigation of fault modelling in the identification of bearing wear severity

Recent research into machines involved in the power generation process has demanded deep investigation of model-based techniques for fault diagnosis and identification. The improvement of critical fault characterisation is crucial in the maintenance process effectiveness, hence in time/costs saving, increasing performance and productivity of the whole system. Consequently, this paper deals with a common fault in hydrodynamically lubricated bearings assembled in rotating systems, namely, that of abrasive wear. Research on this topic points to an interesting query about the significance of model detail and complexity and the identification of its characteristic parameters for the important stages of fault diagnosis and fault identification. For this purpose, two models are presented and analysed in their completeness concerning the fault signature by vibration measurements, as well as the identification of fault critical parameters which determine the machine lifetime estimation, maintenance procedures and time costs regarding performance and productivity. From this study, the detailing in fault modelling has a substantial impact on fault parameter identification, even if its improvement is not so expressive in fault diagnosis procedures involving standard signal processing techniques of vibration signatures.

On Crack Detection in a Laminated Glass/Epoxy Composite Beam under Free Vibration with Fuzzy Logic Aid

This study focuses on developing and implementing Mamdani hybrid fuzzy logic inference system (FIS) for transverse crack detection and fault diagnosis in a woven fiber laminated glass/epoxy composite beam using different vibration modes of natural frequencies. The shifting of vibration is attributed to the implication of cracks. These vibration signatures are fuzzified through hybrid fuzzy sets (triangular, trapezoidal, Gaussian) and scaled to crack location and depth using the fuzzy rules and defuzzification process. The vibration signatures are recorded using ABAQUS finite element (FE) simulation software for a fixed beam and are fed as input parameters to the developed FIS for computing the desired outputs. The realization for crack depth and position is experimentally verified through a Fast Fourier Transform (FFT) analyzer. The experimental results with simulated data show that fuzzy logic application detects crack positions and depth accurately at different levels. It is concluded that the hybrid FIS bears a close resemblance to the experimental analysis and also stands out as an effective method for crack detection in LCB over other standalone methods. The current method can be used as a cost-effective non-destructive technique for health monitoring and fault diagnosis of composite beam structures in any practical field.

Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach

Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.

Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy

An imperative requirement of a modern machining system is to detect tool wear while machining to maintain the surface quality of the product. Vibration signatures emanating during machining with a single point cutting tool have proven to be good indicators for the tool’s health. The current research undertaken utilizes vibration signatures while turning EN9 and EN24 steel alloy to predict tool life using Artificial Neural Network (ANN). During initial meager experimentation, tool acceleration during machining was recorded, and the width of the flank wear at the end of each run was measured using Tool Makers Microscope. The recorded experimental data is utilized to develop the neural network with the variation of operating parameters and corresponding tool vibration with measured tool flank wear. The endeavor undertaken for the development of ANN flank wear prediction model was effective with a regression coefficient of 0.9964. The proposed methodology of indirect measurement of tool wear is efficient, economical for the machining industry to predict tool life, which in turn avoids catastrophic tool failure.

Magnetoresistive Sensors and Piezoresistive Accelerometers for Vibration Measurements: A Comparative Study

This experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications.

Vertical Vibrations of Footbridges Due to Group Loading: Effect of Pedestrian–Structure Interaction

The vibration serviceability of footbridges has evolved from the adoption of a single pedestrian crossing in the resonance condition to load cases in which several pedestrians cross the structure simultaneously. However, in spite of this improvement, pedestrians continue to be considered as applied loads in codes of practice. Recent research has pointed out that modeling pedestrians as dynamic systems is a step further in the search for a more realistic design approach. This is explored in this paper, focusing on the case of vertical vibration. A two-span cable-stayed test structure was selected, and accelerations were measured from single and group crossings, both at the structure and at a pedestrian’s waist. Numerical simulations considering the pedestrians modeled as loads only and also as dynamic systems were implemented, and numerical and experimental time response vibration signatures were compared. Reductions of up to 25% and 20% in peak and RMS acceleration, respectively, were obtained when pedestrians were modeled as dynamic systems, in comparison with the less realistic model of pedestrians as loads only. Such reductions were shown to depend on the number of pedestrians involved in the group. The results, thus, highlight that pedestrian–structure interaction is an asset for the vibration serviceability design of footbridges.

A review of vibro-acoustic techniques for control of combustion engine noise

The present work focuses on various vibro-acoustic signals techniques that can monitor malfunctions in Internal Combustion Engines (ICEs). Recent works by other authors have focused on various reciprocating machines including compressors no attempts has been deal with IC engines. This paper gives a summary of the generation mechanism of sound and vibration in Engines. An overview of the monitoring and diagnostic techniques base on noise, pressure an vibration signatures is also discussed. Various fault conditions are described which affect ICEs. Measuring of acoustic signals has non-intrusive behavior with capability of detecting airborne transmission paths faults. In view of industrial needs to reduce the maintenance costs monitoring of vehicle operations, the present work can be a useful guide for engineers for understanding types of faults so as sufficient time is obtained to process reliable and information.