AnomAlert Motor Anomaly Detector is a system of software and networked hardware that continuously identifies faults on electric motors and their driven equipment.
AnomAlert Motor Anomaly Detector is a system of software and networked hardware that continuously identifies faults on electric motors and their driven equipment. AnomAlert utilizes an intelligent, model-based approach to provide anomaly detection by measuring the current and voltage signals from the electrical supply to the motor. It is permanently mounted, generally in the motor control center and is applicable to 3-phase AC, induction or synchronous, fixed or variable speed motors.
AnomAlert detects electrical faults and some mechanical faults, providing a diagnostic solution in situations where dedicated vibration monitoring is not practical or economical. For example, it can detect changes in the load the motor is experiencing due to anomalies in the driven equipment or process such as cavitation or plugged filters and screens. Since it doesn’t require any sensor installation on the motor itself or associated load, AnomAlert is especially attractive for inaccessible driven equipment and is applicable to most types of pumps, compressors, and similar loads.
Each motor requires one AnomAlert,a network connection, and current and potential transformers. It can be configured entirely from the front panel. Additionally, each AnomAlert includes the AnomAlert Software necessary to obtain and display data in real-time from the device, to configure the performance of the device, and to save and subsequently retrieve data for display from its database. Networking protocols enable monitoring of motors and processes on remote machines using TCP/IP protocols over the Ethernet.
Please contact +6221-5467618, +6221-54212399 or email [email protected]
AnomAlert is well-suited for rolling element bearing motors and driven loads for a variety of applications, including:
Jalan Boulevard Raya Blok BA3 No.15 Gading Serpong Tangerang 15810, Jakarta, Indonesia
Fax : +6221-5467617
Email : [email protected]