For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties i...For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.展开更多
In this article, an inter-antenna inter-subblock shifting and inversion (IASSI) scheme is proposed to reduce the peak-to-average power ratio (PAPR) in multi-input multioutput orthogonal frequency division multiple...In this article, an inter-antenna inter-subblock shifting and inversion (IASSI) scheme is proposed to reduce the peak-to-average power ratio (PAPR) in multi-input multioutput orthogonal frequency division multiplexing (MIMO- OFDM) systems. It exploits multiple antennas and subblocks to provide additional degrees of freedom to benefit the system. To reduce the implementation complexity of the proposed scheme, two simple suboptimal schemes are further presented based on the minimum current maximum criterion; one adopts sequential search and the other employs random binary grouping. The simulation results exhibit the effectiveness of these proposed schemes.展开更多
Previous test sequencing algorithms only consider the execution cost of a test at the application stage. Due to the fact that the placement cost of some tests at the design stage is considerably high compared with the...Previous test sequencing algorithms only consider the execution cost of a test at the application stage. Due to the fact that the placement cost of some tests at the design stage is considerably high compared with the execution cost, the sequential diagnosis strategy obtained by previous methods is actually not optimal from the view of life cycle. In this paper, the test sequencing problem based on life cycle cost is presented. It is formulated as an optimization problem, which is non-deterministic polynomial-time hard (NP-hard). An algorithm and a strategy to improve its computational efficiency are proposed. The formulation and algorithms are tested on various simulated systems and comparisons are made with the extant test sequencing methods. Application on a pump rotational speed control (PRSC) system of a spacecraft is studied in detail. Both the simulation results and the real-world case application results suggest that the solution proposed in this paper can significantly reduce the life cycle cost of a sequential fault diagnosis strategy.展开更多
基金Supported by Humanities and Social Science Programme in Hubei Province,China(Grant No.14Y035)National Natural Science Foundation of China(Grant No.71203170)National Special Research Project in Food Nonprofit Industry(Grant No.201413002-2)
文摘For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.
基金the National Natural Science Foundation of China(NSFC 60496312);the Hi-Tech Research and Development Program of China(2006AA01Z260);KDDI company of Japan.
文摘In this article, an inter-antenna inter-subblock shifting and inversion (IASSI) scheme is proposed to reduce the peak-to-average power ratio (PAPR) in multi-input multioutput orthogonal frequency division multiplexing (MIMO- OFDM) systems. It exploits multiple antennas and subblocks to provide additional degrees of freedom to benefit the system. To reduce the implementation complexity of the proposed scheme, two simple suboptimal schemes are further presented based on the minimum current maximum criterion; one adopts sequential search and the other employs random binary grouping. The simulation results exhibit the effectiveness of these proposed schemes.
基金supported by China Civil Space Foundation(No.C1320063131)
文摘Previous test sequencing algorithms only consider the execution cost of a test at the application stage. Due to the fact that the placement cost of some tests at the design stage is considerably high compared with the execution cost, the sequential diagnosis strategy obtained by previous methods is actually not optimal from the view of life cycle. In this paper, the test sequencing problem based on life cycle cost is presented. It is formulated as an optimization problem, which is non-deterministic polynomial-time hard (NP-hard). An algorithm and a strategy to improve its computational efficiency are proposed. The formulation and algorithms are tested on various simulated systems and comparisons are made with the extant test sequencing methods. Application on a pump rotational speed control (PRSC) system of a spacecraft is studied in detail. Both the simulation results and the real-world case application results suggest that the solution proposed in this paper can significantly reduce the life cycle cost of a sequential fault diagnosis strategy.