Kang B. Lee

Spindle Health Diagnosis Based on Analytic Wavelet Enveloping

A new diagnostic technique for identifying structural defects in spindles was developed based on the analytic wavelet transform. The new technique extracts defect-induced impulses from the spindle vibration signal and constructs their envelopes in a single step, eliminating the need for intermediate operations as traditionally required. Theoretical background of the analytic wavelet transform was first introduced, and numerical simulation was then conducted on a synthetic signal. The result was subsequently compared with vibration signals measured on a spindle test bed. It was confirmed that the developed technique is effective in detecting defect-induced impulses buried in the spindle vibration signals that otherwise were undetectable using the traditional spectral techniques

Sensor network and information interoperability integrating IEEE 1451 with MIMOSA and OSA-CBM

The IEEE 1451 smart transducer interface for sensors and actuators standard was developed to facilitate sensor to network interoperability. It has defined a set of common communication interfaces for connecting transducers (sensors or actuators) to control networks and instruments in a network-independent environment. Concurrently, the Machinery Information Management Open Systems Alliance (MIMOSA) has established an open architecture and a set of protocols for exchanging complex sensor information between condition-based maintenance (CBM) systems. Likewise, the open system architecture for condition based maintenance (OSA-CBM) program has developed an open architecture and standard for distributed CBM software components. Ideally, if there is a way to interface these standards, sensor information can be easily accessed by condition-based maintenance application software. This paper examines the IEEE 1451 standard and the MIMOSA/OSA-CBM specifications to find entry points that can provide a connection between these standards.

An IEEE 1451.5–802.11 standard-based wireless sensor network with embedded WTIM

This paper introduces a reference implementation of the Institute of Electrical and Electronics Engineers (IEEE) 1451.5–802.11 standard-based wireless sensor network (WSN) developed at the National Institute of Standards and Technology (NIST). The WSN consists of a Network Capable Application Processor (NCAP) and two Wireless Transducer Interface Modules (WTIM). The NCAP, a gateway node of the WSN, was developed on a laptop in Java language according to the IEEE 1451.5–802.11 standard. The embedded WTIM, a wireless sensor node, was developed based on the IEEE 1451.5–802.11 standard on a single board computer in Dynamic C language. The wireless communications between the NCAP and WTIMs are based on IEEE 1451.0 messages using Transmission Control Protocol/Internet Protocol (TCP/IP) and User Datagram Protocol/Internet Protocol (UDP/IP) sockets. A few examples are provided to illustrate the functionalities of the WSN.

An implementation of a wireless sensor network based on IEEE 1451.0 and 1451.5–6LoWPAN standards

This paper describes an implementation of the IEEE 1451.0 and 1451.5 standards for smart transducers. The implementation, developed at the National Institute of Standards and Technology (NIST), uses Internet Protocol Version 6 (IPv6) over Low power Wireless Personal Area Networks (6LoWPAN) hardware to demonstrate the capabilities of the 1451 family of standards. The network consists of a Network Capable Application Processor (NCAP), implemented in Java, working with five Wireless Transducer Interface Module (WTIM) nodes. In operation, the NCAP communicates with WTIMs via the 1451.0 and 1451.5 interfaces over 802.15.4 using 6LoWPAN to implement User Datagram Protocol (UDP) IPv6 architecture. In this paper, case studies demonstrate the implementation of WTIM announcements, the client-server ability to read both binary and text-based Transducer Electronic Data Sheet (TEDS), the ability to read sensor data, and an alert function. This work demonstrates some possibilities and features a low-power wireless network can provide when using the IEEE 1451.0 and 1451.5 standards.

Condition Monitoring of Operating Spindle Based on Stochastic Subspace Identification

Accurate identification of spindle modal parameters is critical to realizing a new generation of “smart” machine tools with built-in self-diagnosis capability. This paper describes a new approach to extracting spindle modal parameters from the output measured during operation, based upon stochastic subspace identification. The technique accounts for structural dynamic behavior, associated with the spindle rotation, that is not present when the spindle remains stationary. Experimental results conducted on a customized spindle test bed under different speed-load combinations confirm the effectiveness of the new technique for on-line spindle condition monitoring.Copyright

A module-based software system for spindle condition monitoring

Accurate identification of spindle working conditions is one of the key features of the next generation smart machining systems with built-in self-diagnosis capability. This paper presents a module-based software system for online spindle defect identification and localisation through an analytic wavelet envelope spectrum algorithm. The software is designed in accordance with the architectural structure of OSA-CBM, and implemented using the graphical programming language LabVIEW. Spindle condition is displayed online in both a basic window for machine operators and a diagnosis window for advanced analysis. The software provides a user-friendly human-machine interface and contributes to realising a smart machine tool.

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.Copyright