BACKGROUND

Field

The present disclosure is generally directed to predictive maintenance systems, and more specifically, to execution of multi task learning with incomplete labels for predictive maintenance.

Related Art

Predictive Maintenance (PdM) is a widely-adopted maintenance practice which is based on continually monitoring the condition of the equipment with the goal of determining the right maintenance actions to be taken at the right times. With the advance of Internet of Things (IoT) and its applications to industrial environments, data analytics algorithms can be applied to the data coming from equipment in real time in order to provide actionable insights about equipment health and performance and predict impending failures and downtime. The use of data-driven technologies for predictive maintenance increases equipment availability, reduces the cost of maintenance, and improve the safety of equipment operators.

Predictive maintenance involves the solution of several related problems which include (but not limited to), Remaining useful life (RUL) estimation as a long-term task which estimates how much time is left in the useful life of the equipment, Failure prediction (FP) which estimates the probability that a failure is going to happen within a typically short time horizon, Fault detection (FD) which detects faults as soon as they happen, and Performance degradation detection (PDD) which detects slow degradation in the performance of the equipment.

These predictive maintenance problems are typically solved independently using separate models, which is problematic for real-world applications. When these models are applied in practice to the same component simultaneously, this might lead to inconsistent results. For example, failure prediction model can predict that a failure will happen in five days with 80% probability, while RUL model may predict that there is 10 days until the failure concurrently. Taking a closer look of these models, it is clear that they are usually trained on the same input data, share similar features, and give highly correlated outputs. For example, RUL estimation and failure prediction (FP) are about predicting when the failure event is going to happen, and fault detection is about learning abnormal events which are usually strong indicators of failures in a near future. Performance degradation detection model evaluates general health conditions by giving a degradation curve, which indicates fault or failure when significant degradations are observed.

SUMMARY

In order to exploit the relationship between these different time horizons and give consistent results, example implementations described herein involve using multi-task learning (MTL) to exploit the implicit relationship between tasks to learn the underlying failure/fault distribution and system dynamics. However, related art multi-task learning algorithms require the availability of all the labels associated with each task for every input signal (e.g., time to failure, likelihood of failure, performance indicator value, etc.). Such requirements can be infeasible in practical systems as typically one label is available for a certain predictive maintenance task at a given point of time and other labels might not be available or even applicable to obtain (e.g., having time to failure labels only so that RUL and FP tasks can be performed but performance degradation detection is impossible due to the lack of KPI labels). Such incomplete label information cannot be easily handled by related art MTL approaches.

Example implementations described herein involve a novel learning methodology for MTL to solve a large variety of PdM tasks with incomplete labels. In addition, the developed approach is capable of modeling multiple type of subjects (e.g., various failure modes, faults, or degradation), which differentiate the example implementations from related art approaches of unified methods that can only deal with a single failure mode.

Example implementations utilize MTL with a novel learning methodology for handling multiple predictive maintenance tasks with incomplete label information. This approach allows different predictive maintenance tasks such as failure prediction, remaining useful life estimation, fault detection and performance degradation detection to be learned simultaneously within a single model to ensure consistent predictions while being capable of learning with incomplete label information.

In particular, the example implementations described herein involve the following aspects.

Aspects of the present disclosure include utilizing MTL as a unified approach for a large range of predictive maintenance tasks such as failure prediction, remaining useful life estimation, fault detection and performance degradation detection, while providing consistent results.

Aspects of the present disclosure involve a novel learning methodology to handle incomplete label information which is a common problem in industrial applications, without losing the capability of solving multiple tasks.

Aspects of the present disclosure involve utilizing the relationship between the tasks working at different time-horizons, so that the example implementations builds only one model working for multiple failure modes and fault types simultaneously.

Aspects of the present disclosure involve a novel constraint loss to use the non-failure (censored) data in RUL estimation, which is an unsolved problem in previous related art studies. Without any need for a pre-prediction step of a dedicated unsupervised learning procedure, this new method in accordance with the example implementations described herein provides a much more practical utility and an ability to be deployed.

Aspects of the present disclosure involve a method, which can involve, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for a plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data from the one or more components.

Aspects of the present disclosure involve a non-transitory computer readable medium, storing instructions for executing a process, the instructions involving, for data having incomplete labeling to generate a plurality of predictive maintenance models, processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and executing the predictive maintenance models on subsequently recorded data from the one or more components.

Aspects of the present disclosure involve a system involving, for data having incomplete labeling to generate a plurality of predictive maintenance models, means for processing the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and means for executing the predictive maintenance models on subsequently recorded data from the one or more components.

Aspects of the present disclosure involve a system, involving one or more components; and an apparatus configured to receive data from the one or more components, the apparatus involving a processor, configured to, for the data having incomplete labeling to generate a plurality of predictive maintenance models, process the data through a multi-task learning (MTL) architecture including generic layers and task specific layers for the plurality of predictive maintenance models configured to conduct tasks to determine outcomes for the one or more components associated with the data, each task specific layer corresponding to one of the plurality of predictive maintenance models; the generic layers configured to provide, to the task specific layers, associated data to construct each of the plurality of predictive maintenance models; and execute the predictive maintenance models on subsequently recorded data from the one or more components.

