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• The research and application of intelligent technology in equipment fault diagnosis. • A fuzzy neural network model suitable for fault diagnosis is established. • A fuzzy inference method based on fuzzy network is constructed. In recent years, mechanical fault diagnosis technology at home and abroad has developed rapidly, and its application has spread to various industrial fields. Due to the complex structure of rotating machinery, the ambiguity and complexity of fault characteristics and causes are common, and it is difficult to carry out fault diagnosis. Although many researches have been carried out and some research results have been obtained, the overall diagnostic level is not very high. High, which is extremely inconsistent with the status quo that is widely used in production. Therefore, it is of great significance to carry out fault diagnosis research on rotating machinery. In this paper, this paper briefly introduces the research and application of intelligent technology in equipment fault diagnosis, and gives the superiority of fuzzy neural network technology application in equipment fault diagnosis, and expounds the basics of fuzzy theory and neural network technology. Based on the principle, the advantages and disadvantages of the two in fault diagnosis are analyzed, and the necessity of combining the two is explained. Based on the previous research on the combination of fuzzy theory and neural network, a new combination method is proposed, and a fuzzy neural network model suitable for fault diagnosis is established. A fuzzy inference method based on fuzzy network is constructed, which realizes the knowledge of information through the extraction, optimization and screening of fuzzy rules. At the same time, the fuzzy neural network learning weights are transformed into case-based reasoning-based diagnostic guidance operators, which play an important role in the rapid extraction of knowledge and improve the diagnostic accuracy. The experimental results show that compared with the commonly used neural network and fuzzy theory fault diagnosis methods, this method can make up for the shortcomings of fuzzy theory and neural network alone. It has higher diagnostic accuracy and has a good application prospect in the field of rotating machinery fault diagnosis. [ABSTRACT FROM AUTHOR]

Preventive measures must be implemented to preclude severe damage to bridge structures and thus avoid catastrophic accidents. A potential method for bridge damage identification is the characterization of structures through influence lines. However, influence line detection studies related to arch bridge damage are not sufficiently comprehensive. In this paper, a displacement difference influence line damage detection method for tied-arch bridge hangers is proposed. First, based on the structural characteristics of the arch bridge hanger, an elastic support beam model assumption is developed. Then, the relationship between local damage and extent of damage (DE) to the arch bridge hanger based on the displacement influence line is established through analytical derivation. Subsequently, the influence of hanger damage location and DE was analyzed using the MIDAS software finite element model. A reasonable vehicle loading test scheme is also proposed. The influence of factors, such as load spacing, loading path, measurement location, axle load mode and noise error, on identifying the damage to arch bridge hangers is examined by controlling a single variable and conducting a comparative study. Finally, the sensitivity of arch bridge component damage to hanger damage is investigated through finite element verification and gray correlation analysis. Hence, the proposed detection method is found capable of identifying local structural damage to arch bridge hangers. [ABSTRACT FROM AUTHOR]

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Abstract: For machine fault diagnosis the signals from working machine are always numerous, even uncountable, but there contains only a little useful information. Hence how to find out the useful signal from numerous signals, including noises, that is, how to only extract the desired fault signal is very attractive. This paper shows that the constrained independent component analysis (cICA) can solely extract desired faulty signal using some prior mechanical information. The methods of creating reference of cICA for machine diagnostics are discussed, and the effectiveness of the method is successfully verified by simulations and experiments. [Copyright &y& Elsevier]

• A novel BTC measurement method based on event capture technique is proposed. • BTC values are solved based on the relationship between the counts of events and BTC distribution. • Accuracy, sensitivity, range and stability are demonstrated by the relationship equations. • A test to validate the proposed method in BTC measurement for a turboengine is performed. • Comparing with continuous sampling method, the measurement error is within 0.01 mm, and hardware occupation is reduced to 0.1%. Blade tip clearance (BTC) measurement is an essential technology in turbomachinery health monitoring so as to enhance efficiency and reliability as well as to ensure timely maintenance. The existing BTC measurement methods, which based on continuous sampling technique, depend highly on the devices sampling frequency, data transmission speed, storage memory, and computation capability, can hardly achieve online real-time measurement and long-term continuous monitoring. BTC distribution of a turbomachinery possesses discreteness, symmetry, monotonicity and quasi-periodicity, based on these characteristics, this paper proposes a novel BTC measurement method based on event capture technique. The method sorts the sensors signal into distinct events according to pre-set trigger thresholds, describes the relationship between the event kinds and the BTC distribution via mathematical equations, and solves the BTC values accordingly. The measurement error, measuring sensitivity, measurable range, and solving stability of the proposed method are demonstrated. A test to validate the proposed method for BTC measurement in the inlet section of a turboengine is performed, and the results show that, the event capture technique is capable of achieving fast and accurate BTC measurement. The proposed methods own a measurement error within 0.02 mm (for dual-channel configuration) and 0.01 mm (for multi-channel configuration) comparing with the continuous sampling technique. Meanwhile their hardware resource requirement are reduced to 0.02% (for dual-channel configuration) and 0.1% (for multi-channel configuration) comparing with the continuous sampling technique. For processing these performances, the proposed method can be applied for online real-time BTC measurement and long-term continuous BTC monitoring. [ABSTRACT FROM AUTHOR]