Equipment Fault Detection Research Articles

The Software Demo of Acoustic Imaging for Equipment Fault Detection

Equipment faults often produce the abnormal sound; the acoustic imaging detection can test the equipment faults remotely and visually. In many applications, especially in safety related area, it can extern the monitor fields and improve detecting ability. The function of software design is to show the photo and sound imaging. In this paper, acoustic imaging software design method of equipment fault diagnostic is proposed. The difference in the time of arrival (TDOA) is used to identify the location of faults. And in a software demo, the two USB microphones and a microphone in PC are used to acquire the sound. The spectral photo and sound is mapped to locate the fault sources.

Fault detection method for magnetic resonance imaging spectrometer based on the acquisition and analysis of its output waveforms

AbstractIn the development of a magnetic resonance imaging spectrometer, the equipment fault detection methods are mainly reliant on visual inspection of reconstructed images or k‐space data, combined with observation of the output waveforms via an oscilloscope. However, when using the above methods, it may be quite difficult to determine minor design flaws that would produce image ghost or other problems. This article presents a fault detection method that is based on acquisition and analysis of the output waveforms from the spectrometer. While a sequence is running, the spectrometer outputs, including the digital gate and the gradients, are sampled using a data acquisition card. The acquired data is then processed using a high‐performance graphic processing unit to allow the feature points, which are the endpoints of the waveform segments in this design, to be extracted. The processing operation is composed of data filtering, differencing, and clustering. Finally, the extracted feature points are compared with the predefined feature points of the sequence to determine any design errors. This method has been used to solve image ghost problems in our home‐built spectrometer.

Accelerated Tests of Soft Errors in Network Systems Using a Compact Accelerator- Driven Neutron Source

The frequency of neutron-induced soft errors is increasing as devices become more integrated and miniaturized. Therefore, it has become more important recently to check reliability of a recovery system from the soft errors in network systems. For accelerated test, first we have examined possibility of the acceleration tests at a compact accelerator-driven neutron source, which is easy to adjust for soft-error tests and which also has low experimental costs. We selected the electron accelerator-driven neutron source at Hokkaido University as the compact accelerator-driven neutron source. We prepared a new target-reflector assembly composed of heavy metals to provide the fast neutrons, and conducted neutron-induced soft-error experiments on network equipments. As a result, we found that an accelerated rate of soft errors was about $10^{{\mathbf {6}}}$ times compared with that of the natural environment. We also investigated network equipment soft-error tolerance, fault detection and backup switching processes. Performing such testing before network equipment is actually deployed is critical for development of future network systems. Hence, the compact accelerator-based neutron source is a very useful tool.

Detection of Equipment Faults at Sequencing Batch Reactor Using Dynamic Time Warping

Detection of Equipment Faults at Sequencing Batch Reactor Using Dynamic Time Warping

Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process

Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process

Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process

Semiconductor manufacturing is one of the most technologically and highly complicated manufacturing processes. Traditional machine learning algorithms such as uni-variate and multivariate analyses have long been deployed as a tool for creating predictive model to detect faults. In the past decade major collaborative research projects have been undertaken between fab industries and academia in the areas of predictive modeling. In this paper we review some of these research areas and thus propose machine learning techniques to automatically generate an accurate predictive model to predict equipment faults during the wafer fabrication process of the semiconductor industries. This research paper aims at constructing a decision model to help detecting as quickly as possible any equipment faults in order to maintain high process yields in manufacturing. In this research, we use WEKA tool and R languages for implementing our proposed method and other five machine learning discovery techniques.

Mathematical model of flaw detection

Analyzing the efficiency of preventive measures based on probability relations of reliability theory demonstrates that these measures principally rank below nondestructive testing (defectoscopy, or flaw detection), which represents a direct diagnostic method for preventing failures and accidents. The proposed mathematical model rests on the interpretation of nondestructive testing as observations of the current state of the basic reliability parameter, namely, the failure rate. Analytical relations of the model are obtained using Kolmogorov’s equations for the limiting probabilities of the state graph of an item–defectoscope system. The model has the following parameters: the failure and restoration rates of an item, the probabilities of errors of the first and second kind of the faults' detection, and the frequency of item testing. The results demonstrate the technological and economic efficiency of flaw detection. Here, the frequency of testing is more important for economic efficiency than small probabilities of errors. The model can be used to optimize performance specifications for the design of flaw detection equipment. The numerical examples approximately correspond to conditions of offshore industry.

Redundancy and lack of information in specific problems of GNSS measurement processing

The paper focuses on four problems of GNSS measurement processing. The difficulty of problems lies in large volume of available data and limited resources of onboard equipment (phase measurement ambiguity resolution, fault detection and isolation); or in insufficient volume and fragmentarity of data (multiantenna heading determination in constrained conditions, navigation of geostationary satellites). Theoretical foundations of proposed solutions are given. Experimental results are provided.

Fault Diagnosis of Mechanized Bridge’s Electrical System Based on I<sup>2</sup>C-bus and Virtual Instrument Technology

The electrical system fault testing and diagnosis system based on I2C-bus and embedded equipment is proposed. This system is accomplished with the integration of PXI-based computer, field fault testing platform, dedicated interface adapter unit and accessories with software program of virtual instrument and database management and fault guidance system. It can obtain various state parameters of the heavy mechanized bridge by interface adapter unit and corresponding sensors. The dedicated interface adapter unit, that is the core of the hardware platform is composed of chassis, embedded microcomputer controller, I2C-bus data acquisition modules, VPC connection package and military aviation sockets. Because of its flexibility and modular design, it can support different data conversion, for example, analog, digital, and serial, while functioning as an intelligent conversion bridge between field fault testing platform or PXI computer and electrical system of heavy mechanized bridge electrical system. The fault diagnosis software is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with fault repair and diagnosis database. Therefore it provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, as well as repair and maintenance guidance The fault testing and diagnosis system in this work is also featured with handy structure, lightweight, low consumption, large storage capacity, excellent expandability and intelligent inference capability. The system can be used in the online fault testing and diagnosis of similar engineering equipments. So it has important engineering and military application value to the rapid fault testing and fault diagnosis of engineering equipments.

Design and Implementation of Fault Detection Equipment of Certain Type of Microwave Range Finder

Certain type of microwave range finder has been designed by adopting the technologies of DDS (direct digital frequently synthesis) digital signal synthesis and online signal detection, synthetically applying equal-accuracy frequency measuring method, automatic control of frequncy drift and quick interpolation method, combining WINDOWS API programming, Labview programing and fault disgnosis expert system. Eventually, accuracy verification and fault defect of microwave range finder are accomplished.

Regression Methods for Virtual Metrology of Layer Thickness in Chemical Vapor Deposition

The quality of wafer production in semiconductor manufacturing cannot always be monitored by a costly physical measurement. Instead of measuring a quantity directly, it can be predicted by a regression method (virtual metrology). In this paper, a survey on regression methods is given to predict average silicon nitride cap layer thickness for the plasma-enhanced chemical vapor deposition dual-layer metal passivation stack process. Process and production equipment fault detection and classification data are used as predictor variables. Various variable sets are compared: one most predictive variable alone, the three most predictive variables, an expert selection, and full set. The following regression methods are compared: simple linear regression, multiple linear regression, partial least square regression, and ridge linear regression utilizing the partial least square estimate algorithm, and support vector regression (SVR). On a test set, SVR outperforms the other methods by a large margin, being more robust toward changes in the production conditions. The method performs better on high-dimensional multivariate input data than on the most predictive variables alone. Process expert knowledge used for a priori variable selection further enhances the performance slightly. The results confirm earlier findings that virtual metrology can benefit from the robustness of SVR, an adaptive generic method that performs well even if no process knowledge is applied. However, the integration of process expertise into the method improves the performance once more.

Study on Intermittent Faults and Electrical Continuity

Intermittent faults in electrical/electronic interconnections are recognised as one of the major sources of No Fault Found (NFF) events. These can be caused due to surface corrosion (e.g. oxidation of pins or fretting wear), bent pins, debris within the female connector, or incorrect installation during initial manufacture and assembly. Unless such issues are narrowed down to a specific root cause, any corrective actions or troubleshooting will be difficult to carry out, and hence its resolution may not make its way into future designs of the system. This leads to further susceptibility to NFF. Intermittent behaviour is often a clear sign of a partially damaged connector, or a connector undergoing a particular degradation mechanism, with the level of intermittency being further aggravated through process variation of harsh environments and parametric faults. In order to further our understanding of the relationship between degradation, operating conditions, intermittent behaviour within the subject, an experimental investigations have been carried out.This paper is a work in progress paper that illustrates a test regime that has been used to stimulate intermittence in electronic connectors whilst subjected to vibration, using both a traditional oscilloscope and bespoke intermittent fault detection equipment, in order to capture an intermittent signature. The results of these experiments provide an insight into the limitations of test equipment and requirements for future intermittent fault detection techniques.

An ICA-Based Audio Feature Fault Detection Method for Transformer Equipments

Experienced engineers in transformer substation can judge the equipment condition via just listening to the working sounds of electrical equipments. Use audio signal processing applied in engines and other mechanical equipments for reference. A scheme to monitor the working condition of electrical equipments is proposed. Firstly, the basic principles and system structure of this scheme is outlined. It introduces the method of colleting electrical equipments working sounds by Microphone array, because Microphone array form a beam to target the source sound, which can reduce the noise and reverberation. When substation is working, the environmental background interference sounds exist and are independent from electrical working sound. So we can use FastICA algorithm that is based on the largest negentropy to separate the collected sound to several independent source signals. It has the advantage of fast convergence and robust. The simulation result shows this algorithm can effectively separate the multiple independent source signals. The separation accuracy is above 95% for typical sample mixed sounds and the reliability of electrical equipment fault detection system based on audio signal processing is ensured.

A High-precision Hand-held Face Detection System

This paper aims at portable high-precision surface defect detection equipment designed based on laser non-contact measurement, though a CCD camera and dual word line semiconductor lasers can achieve the measurement of small surface in a close distance. Resultant data is sent to PC via wireless transmission mode to achieve the separation of detection module with terminal display processing module. The detection system with a small-volume structure which can achieve fast and accurate scanning can be used to achieve the device workpiece surface defect measurement under various complex occasions. It is able to achieve real-time display of the measurement results through MATLAB graphical user interface. High measuring accuracy and reliability of the system is verified by experiments, and the depth measurement accuracy can reach 40μm. The results provide an important basis for the detection of surface blemish and improve product design and processing quality.

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Supermarket performance monitoring is of vital importance to ensure systems perform adequately and guarantee operating costs and energy use are kept at a minimum. Furthermore, advanced monitoring techniques can allow early detection of equipment faults that could disrupt store operation. This paper details the development of a tool for performance monitoring and fault detection for supermarkets focusing on evaluating the Store's Total Electricity Consumption as well as individual systems, such as Refrigeration, HVAC, Lighting and Boiler. Artificial Neural Network (ANN) models are developed for each system to provide the energy baseline, which is modelled as a dependency between the energy consumption and suitable explanatory variables. The tool has two diagnostic levels. The first level broadly evaluates the systems performance, in terms of energy consumption, while the second level applies more rigorous criteria for fault detection of supermarket subsystems. A case study, using data from a store in Southeast England, is presented and results show remarkable accuracy for calculating hourly energy use, thus marking the ANN method as a viable tool for diagnosis purposes. Finally, the generic nature of the methodology approach allows the development and application to other stores, effectively offering a valuable analytical tool for better running of supermarkets.

The Fault Detection and Diagnosis System of Hydraulic Excavator Based on PXI Interface Technology

The fault detection and diagnosis system, which uses dedicated interface adapter unit and PXI interface modules to perform signal excitation and parameter testing, is introduced in this paper. Its firmware consists of PXI-bus control computer, display control unit, dedicated interface adapter unit, connection cable and power supply unit. The dedicated interface adapter unit (IAU) is the core of hardware platform, and is composed of chassis, I2C-bus data acquisition board, adapter and military aviation sockets. It carries out analog to digital, digital to analog, digital I/O transformation as well as serial communication and CAN-bus communication. The software of the fault detection system is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with repair and diagnosis database. The software provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, repair guidance and data storage. Therefore, this system can implement fault detection of hydraulic excavator on replaceable circuit board and block of the hydraulic system or electrical system. And it can help equipment repairmen and operator perform quick repairs and maintenance to the electrical system and hydraulic circuit of the excavator.

Fault Detection System Overall Design for Artillery Control Device

We designed the fault detection equipment for artillery cotrol device, and the performances and faults of the three subsystems can be detected, according to their signal characteristics. In this fault detcetion system, the MCU is used as the master controller, and the A/D conversion chip MAX197 is applied to acquisit and convers data. The data will be processed by the MCU, then be transfered to LCD intelligent terminal through serial port, and finally be displayed and preserved in the terminal. This detection system can complete data acquisition and real-time fault detection, locating the faults to the specific parts. It can also provide expert maintenance proposal in the three subsystems of artillery control device. Besides, this system has great generality and practicability, and it is convenient for upgrating and reforming.

Study in Ultrasonic Flaw Detection for Small-Diameter Steel Pipe with Thick Wall

In this paper, the detective method, using ultrasonic immersion method with focusing probe, was presented. Based on analyzing and solving the reflection and refraction coefficients of ultrasonic waves at the interface between two different media, the transmission paths were chosen, which were based on selected thick steel pipe (t/D ≥ 0.3), this is so-called converted shear wave testing method of ultrasonic detection of flaws. Moreover, the theory of flaw detection of the pipes was studied. The sound pressure of echo of flaws detected by converted shear wave was deduced, as was verified by independently developed automatic ultrasonic flaw detection equipment for the pipe. Experimental results revealed that it was feasible to detect the flaw by converted shear wave testing method. Echo was clear, the SNR was perfect, and the sensitivity was 4.3dB higher than one detected by refracted transverse wave. Detected-flaw speed was improved resulting in the test time dramatically shortening, which was especially beneficial for overall-length and all-round detection.

Self-oscillation of flaw-detection equipment for arterial gas pipelines

Self-oscillations of an intra-pipe inspection pig are investigated. A dynamic model of the machine is formulated, and its motion regimes are analyzed qualitatively. The law governing the motion of the inspection pig and the law governing the velocity variation corresponding to it are derived.

Design of Transportable Hydraulic System Fault Detection Equipment for Construction Machinery

Based on embeded technology a portable fault detection equipment for the construction machinery hydraulic system is developed. The equipment is mainly composed of embeded hardware platform and graphical-interface software platform. The hardware platform includes the core control unit ECM-945GSE industrial computer, 8-channel data acquisition module based on ARM chip, LCD screen, touch screen, power supply module with large-scale operation voltage and wide temperature range, PTT pressure transducers, the integration transducer of temperature, pressure, flow rate transducer as well as equipment chassis. The software system consists of data acquisition procedures, diagnosis, detection and output printing programs and drivers programs. The pilot trial in the armed forces shows that both detectors can be used in repair shop or indoor, but also in the field of construction machinery on-site emergency repair when rapid detection and diagnosis. And it has a strong adaptability.