Supporting stable operations and improved productivity of customer equipment by realizing efficient maintenance management
HiPAMPS analyzes sensor data collected from equipment and detects unusual statuses. In addition to the functions of HiPAMPS, HiPAMPS-PRO, analyzes past sensor data and failure history information and predicts future failures.
A high operation rate is required for machines and equipment, and if such machines and equipment stop unexpectedly, extensive damages will result for manufacturers and related companies. However, there is a lot of equipment that cannot aid unexpected stoppage, in which data collected for the monitoring status of machines and equipment cannot be effectively used or automatic status monitoring is unavailable due to limited costs.
HiPAMPS-PRO effectively uses sensor data collected from machines and equipment through data mining technology and immediately notifies the user of any variation in the machines and equipment status. It also analyzes past sensor data, information about mechanical equipment and failure history information with the algorithms unique to Cassantec AG that fully utilize statistics and machine learning, providing a remaining useful life estimation service that enables the digitization of the risk of failures that may occur in the future and possible timing.
With this system, we support the implementation of Condition Based Maintenance (CBM) for appropriate maintenance performed in accordance with the equipment status, and contribute to the prevention of unexpected stoppage and the reduction of maintenance costs. In addition, this system currently performs Predictive Analytics (PdA), making it possible to grasp risks regarding the possible time of failures.
In order to satisfy customer needs such as “How long can we use the equipment?” and “We would like to know the risk of failure occurrence,” we will contribute to the improvement of the customer equipment operation rate, maintenance schemes and further reduction of management costs.
HiPAMPS is capable of diagnosing status variations with the data mining function that utilizes statistical data classification as well as the threshold determination function (optional) that utilizes experiences and knowledge as the detection conditions.
<Patent No. : JP 4832609>
The diagnosis engine suitable for your equipment and needs can be selected from the following.
*: These technologies have been developed by Hitachi, Ltd.
The system can be configured by a minimum of one personal computer. It can also be enhanced according to the required functions and performances.
The diagnosis results and machine/equipment status are displayed on the screen in different colors. With this interface, the equipment status can be grasped visually. In addition, the stepwise screen shifting suppresses unnecessary user operation during analysis.
<Patent No. : JP 5081999>
HiPAMPS-PRO provides digitized and visualized data on the risks of possible future failures and facilitates the development of specific maintenance plans.
Because the settings for the information about the equipment load can be changed easily, it enables the simulation of the relationship between the variation of the load on the equipment and the failure timing.
HiPAMPS-PRO computes the remaining useful life (RUL) of your equipment based on analysis results even if data of past operation results are unavailable.
<Patent No. : JP 05771317>
RUL: Remaining Useful Life
The threshold determination function for detecting status variation levels and status variation rates in accordance with input conditions designed by engineers can be used concurrently. This function can grasp if a status variation suddenly occurs in any equipment. In addition, equipment statuses can be explained easily, facilitating the determination of the next action to be taken.
HiPAMPS achieves more accurate predictive diagnosis using the database of maintenance information built when learned data are selected.
Based on the records of past failures and maintenance operations and the related sensor information registered to HiPAMPS, information about past similar incidents is displayed if any failure is predicted. This function allows less time to be spent on estimating the cause of a failure.
HiPAMPS estimates the operable time for the equipment since the detection of signs of failure and predicts the timing of possible failures based on the data of past transitions of equipment statuses.
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