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There are now far more connected devices than people in the world, with an expected 50 billion connected devices by 2020. This is opening up billions of dollars in global economic opportunities, as industries explore innovative ways to use pervasive connectivity, intelligent machines, machine data and advanced analytics to solve new and existing business challenges.
Big Data is the enabler for Hitachi’s innovative Anomaly Detection Technology solutions approach, and we specifically focus on the Internet of Things that matter to business. We explore practical big data applications and how this information can be used to create transformative solutions for enterprises around the world. But that’s only the beginning. We are also anticipating exponential growth in machine-generated data and big data, and we are building and refining advanced analytics solutions to help enterprises draw deeper, faster insights from their data.
Preventive maintenance with periodical inspections and parts replacements. Unexpected failures occur between intervals of periodical maintenance.
Collects sensor data in real-time and predicts potential failures before machines break down.
The anomaly detection system uses our Vector Quantisation Clustering (VQC) and Local Subspace Classifier (LSC) algorithms.
These perform machine learning on normal-status sensor data and create indication of differences between the data to be monitored and the learned normal data group. The system then evaluates whether the result is normal or abnormal.
Traditional methods such as the Mahalanobis-Taguchi (MT) systems can only be applied when sensor data has a normal distribution. Hitachi’s algorithms are resistance to effects from the data distribution.
Since the algorithms are model-free, they can respond flexibly without the need for model construction or simulations for each status change, even when there is a major change in a device or system operation status.
The optimum system configuration uses each algorithm separately according to the device or system to be monitored, or to the characteristics of the abnormality to be detected. A drawback of conventional data mining functions is that causes are difficult to explain when diagnosis results are derived from complex sensor signal correlations.
Therefore, the Hitachi system has been designed to simplify the cause analysis by outputting an ordered lists of the sensor signals responsible for a detected status change.
Why ‘Condition-Based Maintenance’?
Condition-based maintenance is implemented using a combination of condition diagnosis and risk evaluation, leading to effective maintenance without damage to equipment or over-maintenance.
Effective maintenance based on the degradation degree of components. Realise high reliability by avoiding damage to equipment and over-maintenance.
Damage of feed bar causes unscheduled stoppage of printing press.
Advanced predictive diagnosis technology has been applied to condition based maintenance system for gas engine generators on a commercial basis.
|Corporate / Office Name||Hitachi Australia Pty Ltd|
|Address||Suite 801, Level 8, 123, Epping Road, North Ryde NSW 2113 Australia|
|Tel||+61 2 9888 4100 or 1800 HITACHI|
|Fax||+61 2 9888 4188|