Rackham Blg. [Building] construction

Clear view of building and northward. Old St. Joseph hospital visible mid background. No. 13 of chronological series of construction photographs, numbered 1 to 32. Smith, Hinchman & Grylls, architects. W.B. Wood Co., construction

Rackham Blg. [Building] construction

View is toward west. Exterior limestone in progress. No. 14 of chronological series of construction photographs, numbered 1 to 32. Smith, Hinchman & Grylls, architects. W.B. Wood Co., construction

Rackham Blg. [Building] construction

View facing west. Limestone exterior in progress. No. 15 of chronological series of construction photographs, numbered 1 to 32. Smith, Hinchman & Grylls, architects. W.B. Wood Co., construction

Rackham Blg. [Building] construction

Girders and framework added on second floor. Extensive view to north beyond building. No. 16 of chronological series of construction photographs, numbered 1 to 32. Smith, Hinchman & Grylls, architects. W.B. Wood Co., construction

Rackham Blg. [Building] construction

View toward north. Additional steel framework on upper floors. More exterior limestone on main floor. No. 17 of chronological series of construction photographs, numbered 1 to 32. Smith, Hinchman & Grylls, architects. W.B. Wood Co., construction

Rackham Blg. [Building] construction

Most windows in place and entrance steps in progress. No. 32 of chronological series of construction photographs, numbered 1 to 32. Smith, Hinchman & Grylls, architects. W.B. Wood Co., construction

U. of M. League Bldg.

View from the northwest corner showing contruction site with crane lifting metal frame. Korwin negative number 729. On verso: GEO.F.KORWIN COMMERCIAL PHOTOGRAPHER ANN ARBOR, MICH. DIAL 4829 NEGATIVE NO. 729. (circular stamp) On image: U. of M. LEAGUE BLDG. POND & POND, MARTIN & LLOYD ARCH'S. LOVERING-LONGBOTHAM CONT'S MARCH 3, 1928 FROM NW COR. MAIN BLDG. PHOTO BY KORWIN, ANN ARBOR. 729

Lengthened shadows, recounting the development of industrial load transportation beginning at the inception of the electric industrial truck,

"Observing the fiftieth anniversary of the founding of the Elwell-Parker Electric Company, Cleveland, Ohio, 1893-1943."

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

Development of a diagnostic tool for condition monitoring of rotating machinery

This paper presents an overview of the CRC for Infrastructure and Engineering Asset Management (CIEAM)’s rotating machine health monitoring project and the status of the research progress. The project focuses on the development of a comprehensive diagnostic tool for condition monitoring and systematic analysis of rotating machinery. Particularly attention focuses on the machine health monitoring of diesel engines, compressors and pumps by using acoustic emission and vibration-based monitoring techniques. The paper also provides a brief summary of the work done by the three main research collaborating partners in the project, namely, Queensland University of Technology (QUT), Curtin University of Technology (CUT) and the University of Western Australia (UWA). Preliminary test and analysis results from this work are also reported in the paper