Knowledge Center: Asset Optimization

The Center for Intelligent Maintenance Systems (IMS) is working on cutting edge technologies that will ultimately transform maintenance practices from fail-and-fix to predict-and-prevent.

The vision of the National Science Foundation Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS) is to develop a systematic approach in advanced prognostics to enable products and systems to achieve nearzero breakdown reliability and performance.

The Center, a partnership among the Universities of Cincinnati (lead institution), Michigan and Missouri-Rolla, is supported by over 30 global companies including Toyota, GE, GM, Siemens, Boeing, Honeywell, P&G, National Instruments, Advantech, and Omron, to name a few.

The Center’s mission is to serve as a center of excellence for the creation and dissemination of a systematic body of knowledge in intelligent maintenance systems and ultimately to impact next-generation product and service systems with six-sigma quality. It provides timely, high-quality, and costeffective collaborative research projects and validates them through test beds.

Going beyond prediction Most machine maintenance today is either purely reactive – fixing or replacing equipment after it fails, or blindly proactive – assuming a certain level of performance degradation, with no input from the machinery itself, and servicing equipment on a routine schedule whether service is actually needed or not). Both scenarios are extremely wasteful.

It often seems that machines fail suddenly, but in fact they usually go through a measurable process of degradation before they fail. Today, that degradation is largely invisible to users, even though technology has been developed that could make such information visible. It may come as a surprise to many people that most state-of-the-art manufacturing, mining, farming, and service machines (e.g. elevators) are actually quite “smart” in themselves. Many sophisticated sensors and computerized components are capable of delivering data about a machine’s status and performance.

When smart products and machines are networked and remotely monitored, and when their data is modeled and continuously analyzed with sophisticated systems, it is possible to go beyond mere “predictive maintenance” to intelligent “prognostics” – the process of pinpointing exactly which components of a machine are likely to fail, and when, and autonomously trigger service and the order of spare parts. Figure 1 illustrates the focus on product performance degradation assessment and prediction, as well as the key elements of “Intelligent Maintenance Systems (IMS).”

The digital doctorIMS is working on software to “fuse” available data into a more useable, holistic “image” of the actual state of machine performance behavior. A “digital doctor” inspired by biological perceptual systems and machine psychology theory, the Watchdog Agent comprises embedded computational prognostic algorithms and a software toolbox for predicting degradation of devices. It is being built to be extensible and adaptable to most real-world machine situations.

The Watchdog Agent consists of different modules. Each is realized in several different ways to facilitate the use of the Watchdog in a wide variety of products and applications:

Sensory processing module – transforms sensor signals into domains that reveal the product’s performance. Timeseries analysis or frequency domain analysis could be used to process stationary signals (signals with time invariant frequency content), while wavelet or joint time-frequency domains could be used to describe non-stationary signals (signals with timevarying frequency content). Most real-life signals, such as speech, music, machine tool vibration, acoustic emission, are non-stationary signals

Feature extraction module – extracts the features most relevant to describing the product’s performance. Those features are extracted from the domain into which the sensory processing module transforms sensory signals, using expert knowledge about the application, or automatic feature selection methods.

Decision-level sensor fusion – is based on separately assessing and predicting process performance from individual sensor readings, and then merging these individual sensor inferences into a multi-sensor assessment and prediction through an averaging technique.

Performance evaluation module – evaluates the overlap between most recently observed signatures, and those observed during normal product operation. This overlap is expressed through the so-called Confidence Value (CV), ranging between zero and one, with higher CVs signifying a high overlap, and hence performance closer to normal.

Watch & learn If case data associated with some failure modes exists, most recent performance signatures obtained through the signal processing, feature extraction and sensor fusion modules can be matched against signatures extracted from faulty behavior data. This matching allows the Watchdog Agent to recognize and forecast a specific faulty behavior, once a high match with the failure associated signatures is assessed for the current process signatures, or forecasted based on the current and past product’s performance.

Over time, as new failure modes occur, performance signatures related to each specific failure can be collected and used to teach the Watchdog Agent to recognize and diagnose that failure mode in the future. Thus, the Watchdog Agent is envisioned as an intelligent device that utilizes its experience and human supervisory inputs over time, to build its own expandable and adjustable world model.

Even though the performance CV already bares significant prognostic information about the product’s remaining useful life, additional prognostic information can be extracted by capturing the dynamics of the product’s behavior and utilizing it to extrapolate and predict the product’s behavior over time.

At Toyota, the Watchdog Agent was able to predict compressor surge and prevent unexpected downtown and damage to compressor. At Harley-Davidson, the Watchdog Agent, implemented on a Grob Aluminum-cutting machine, was able to automatically convert sensor data to health information and asses degradation and recommend maintenance action.

And the Watchdog Agent was used to monitor the chiller in the control tower of Hong Kong Airport. A radar chart was generated to show the health condition of all the components including shaft, four bearings, evaporator, condenser, compressor oil and refrigerant circuit of the chiller.

Dr Jay Lee is Director of the Center for Intelligent Maintenance Systems (www.imscenter.net); Dr Masoud Ghaffari & Dr Haixia Wang both hold the position of Senior Scientist & Assistant Director.

 

For more information Please visit Honeywell website at www.honeywell.com/ps/sea