公开(公告)号：US20220299985A1

公开(公告)日：2022-09-22

申请号：US17207540

申请日：2021-03-19

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： SERGEY SEREBRYAKOV ,  TAHIR CADER ,  DEEPAK NANJUNDAIAH

IPC: G05B23/02 ,  G06N20/00

Abstract: Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

公开(公告)号：US20230186326A1

公开(公告)日：2023-06-15

申请号：US17550810

申请日：2021-12-14

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： JEFF HANSON ,  TAHIR CADER ,  STEVE ROSCIO

IPC: G06Q30/02

CPC classification number: G06Q30/0201

Abstract: Systems and methods are provided for utilizing a bidirectional intermediate data transport layer to connect multiple customers to a shared, cloud-based AIOps service. As a feature of the intermediate data transport layer, each customer (and their data) may be isolated from other customers. In various examples, the cloud-based AIOps service may receive sensor data from customer systems via the intermediate data transport layer. The cloud-based AIOps service may analyze this sensor data (e.g. detect anomalies, perform root cause analyses, find optimal conditions, etc.), and modify the operation of one or more of the customer systems (e.g. modify the settings/configuration of a sensor on a piece of connected infrastructure) via the intermediate data transport layer. In certain examples, in addition to (or instead of) modifying the operation of one or more of the customer systems, the cloud-based AIOps service may provide an on-premises notification to a customer.

公开(公告)号：US20220404235A1

公开(公告)日：2022-12-22

申请号：US17351085

申请日：2021-06-17

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： TAHIR CADER ,  SERGEY SEREBRYAKOV ,  TORSTEN WILDE ,  JEFF HANSON

IPC: G01M99/00 ,  G06N20/00

Abstract: Systems and methods are provided for improving statistical and machine learning drift detection models that monitor computing health of a data center environment. For example, the system can receive streams of sensor data from a plurality of sensors in a data center; clean the streams of sensor data; generate, using a machine learning (ML) model, an anomaly score and a dynamic threshold value based on the cleaned streams of sensor data; determine, using the ML model and based on the anomaly score and the dynamic threshold value, a correctness indicator for a first sensor in the plurality of sensors; and using the correctness indicator, correct the first sensor.